Single-cell RNA-seq data analysis

# No encoding supplied: defaulting to UTF-8 ?

# R Options
options(stringsAsFactors=FALSE,
        citation_format="pandoc", 
        dplyr.summarise.inform=FALSE, 
        knitr.table.format="html",
        kableExtra_view_html=TRUE,
        future.globals.maxSize=2000000000, mc.cores=4, 
        future.fork.enable=TRUE, future.plan="multicore",
        future.rng.onMisuse="ignore")

# Python3 needed for clustering, umap, other python packages
# Path to binary will be automatically found
# Set manually if it does not work
reticulate_python3_path = unname(Sys.which("python3"))
Sys.setenv(RETICULATE_PYTHON=reticulate_python3_path)

# Required libraries
library(Seurat) # main
library(ggplot2) # plots
library(patchwork) # combination of plots
library(magrittr) # %>% operator
library(reticulate) # required for 'leiden' clustering
library(enrichR) # functional enrichment
library(future) # multicore support for Seurat

# Other libraries we use
# Knit: knitr
# Data handling: dplyr, tidyr, purrr, stringr, Matrix, sctransform, glmGamPoi (optional for speed but only available for R 4.0)
# Tables: kableExtra, DT
# Plots: ggsci
# IO: openxlsx, readr, R.utils
# Annotation: biomaRt
# DEG: mast, limma (for a more efficient implementation of the Wilcoxon Rank Sum Test according to Seurat)
# Functional enrichment: enrichR
# Other: sessioninfo, cerebroApp, sceasy, ROpenSci/bibtex, knitcitations

# Knitr default options
knitr::opts_chunk$set(echo=TRUE,                     # output code
                      cache=FALSE,                   # do not cache results
                      message=TRUE,                  # show messages
                      warning=TRUE,                  # show warnings
                      tidy=FALSE,                    # do not auto-tidy-up code
                      fig.width=10,                  # default fig width in inches
                      class.source='fold-hide',      # by default collapse code blocks
                      dev=c('png', 'pdf'),           # create figures in png and pdf; the first device (png) will be used for HTML output
                      dev.args=list(png=list(type="cairo"),  # png: use cairo - works on cluster, supports anti-aliasing (more smooth)
                                    pdf=list(bg="white")),     # pdf: use cairo - works on cluster, supports anti-aliasing (more smooth)
                      dpi=96,                        # figure resolution
                      fig.retina=2                   # retina multiplier
)

# If the DOI publication servers cannot be reached, there will be no citations, knitcitations will not write a references.bib file and pandoc will stop. This makes sure that there is always at least one citation.
invisible(knitcitations::citep(citation("knitr")))

Dataset description

This section should contain a short description of the experiment and the data.

Project-specific parameters

This code chunk contains all parameters that are set specifically for the project.

param = list()

####################
# Input parameters #
####################
# Project ID
param$project_id = "pbmc"

# Path to input data
param$path_data = data.frame(name=c("pbmc_10x","pbmc_smartseq2"),
                             type=c("10x","smartseq2"),
                             path=c("test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/", "test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/smartseq2/counts_table.tsv.gz"),  
                             stats=c(NA, NA))

# Downsample data to at most n cells per sample (mainly for tests)
#   NULL to deactivate
param$downsample_cells_n = 100

# Path to output directory
param$path_out = "test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/"

# Marker genes based on literature, translated to Ensembl IDs
#   xlsx file, one list per column, first row as header and Ensembl IDs below
#   NULL if no known marker genes should be plotted
param$file_known_markers = "test_datasets/10x_SmartSeq2_pbmc_GSE132044/known_markers.xlsx"

# Annotation via biomaRt
param$mart_dataset = "hsapiens_gene_ensembl"
param$annot_version = 98
param$annot_main = c(ensembl="ensembl_gene_id", symbol="external_gene_name", entrez="entrezgene_accession")
param$mart_attributes = c(param$annot_main, 
                          c("chromosome_name", "start_position", "end_position", 
                            "percentage_gene_gc_content", "gene_biotype", "strand", "description"))
param$biomart_mirror = NULL

# Prefix for mitochondrial genes 
param$mt = "^MT-"

#####################
# Filter parameters #
#####################
# Filter for cells
param$cell_filter = list(nFeature_RNA=c(200, NA), percent_mt=c(NA, 20))

# Filter for features
param$feature_filter = list(min_counts=1, min_cells=3) # feature has to be found by at least one count in one cell

# Samples to drop
# Cells from these samples will be dropped after initial QC
# Example: param$samples_to_drop = c("pbmc_smartseq2_NC", "pbmc_smartseq2_RNA"), 
#   where "pbmc_smartseq2" is the name of the dataset, and "NC" and "RNA" are the names of the subsamples
param$samples_to_drop = c() 

# Drop samples with too few cells
param$samples_min_cells = 10

############################
# Normalisation parameters #
############################
# Which normalisation should be used for analysis?
#   "RNA" or "SCT"
param$norm = "RNA"

# Whether or not to remove cell cycle effects
param$cc_remove = FALSE

# Should all cell cycle effects be removed, or only the difference between profilerating cells (G2M and S phase)?
# Read https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html, for an explanation
param$cc_remove_all = FALSE

# Whether or not to re-score cell cycle effects after data
#   from different samples have been merged/integrated
param$cc_rescore_after_merge = TRUE

# Additional (unwanted) variables that will be regressed out for visualisation and clustering
param$vars_to_regress = c()

# When there are multiple datasets, how to combine them:
#   - method:
#     - "single": Default when there is only one dataset after filtering, no integration is needed
#     - "merge": Merge (in other words, concatenate) data when no integration is needed, 
#                       e.g. when samples were multiplexed on the same chip.
#     - "integrate": Anchors are computed for all pairs of datasets. This will give all datasets the same weight
#                       during dataset integration but can be computationally intensive
#
# Additional options for the "integrate" method:
#
#   - dimensions: Number of dimensions to consider for integration
#   - reference: Use one or more datasets as reference and compute anchors for all other datasets. Separate multiple datasets by comma
#                           This is computationally faster but less accurate.
#   - use_reciprocal_pca: Compute anchors in PCA space. Even more computationally faster but again less accurate 
#                       Recommended for big datasets.
#   - k.filter: How many neighbors to use when filtering anchors (default: min(200, minimum number of cells in a sample))
#   - k.weight: Number of neighbors to consider when weighting anchors (default: min(100, minimum number of cells in a sample))
#   - k.anchor: How many neighbors to use when picking anchors (default: min(5, minimum number of cells in a sample))
#   - k.score: How many neighbors to use when scoring anchors (default: min(30, minimum number of cells in a sample))
param$integrate_samples = list(method="integrate", dimensions=30, reference=NULL, use_reciprocal_pca=FALSE)

# TO DISCUSS from Seurat vignette:
# The results show that rpca-based integration is more conservative, and in this case, do not perfectly align a subset of cells (which are naive and memory T cells) across experiments. You can increase the strength of alignment by increasing the k.anchor parameter, which is set to 5 by default. Increasing this parameter to 20 will assist in aligning these populations.

# The number of PCs to use; adjust this parameter based on the Elbowplot 
param$pc_n = 10

# Resolution of clusters; low values will lead to fewer clusters of cells 
param$cluster_resolution=0.5

#######################################################
# Marker genes and genes with differential expression #
#######################################################
# Thresholds to define marker genes
param$marker_padj = 0.05
param$marker_log2FC = log2(2)
param$marker_pct = 0.25

# Additional (unwanted) variables to account for in statistical tests
param$latent_vars = c()

# Contrasts to find differentially expressed genes (R data.frame or Excel file)
# Required columns:
# condition_column: Categorial column in the cell metadata; specify "orig.ident" for sample and "seurat_clusters" for cluster
# condition_group1: Condition levels in group 1, multiple levels concatenated by the plus character
#                     Empty string = all levels not in group2 (cannot be used if group2 is empty)
# condition_group2: Condition levels in group 2, multiple levels concatenated by the plus character
#                     Empty string = all levels not in group1 (cannot be used if group1 is empty)
#
# Optional columns:
# subset_column: Categorial column in the cell metadata to subset before testing (default: NA)
#                  Specify "orig.ident" for sample and "seurat_clusters" for cluster 
# subset_group: Further subset levels (default: NA)
#                 For the individual analysis of multiple levels separate by semicolons
#                 For the joint analysis of multiple levels concatenate by the plus character 
#                 For the individual analysis of all levels empty string ""
# assay: Seurat assay to test on; can also be a Seurat dimensionality reduction (default: "RNA")
# slot: In case assay is a Seurat assay object, which slot to use (default: "data")
# padj: Maximum adjusted p-value (default: 0.05)
# log2FC: Minimum absolute log2 fold change (default: 0)
# min_pct: Minimum percentage of cells expressing a gene to test (default: 0.1)
# test: Type of test; "wilcox", "bimod", "roc", "t", "negbinom", "poisson", "LR", "MAST", "DESeq2"; (default: "wilcox")
# downsample_cells_n: Downsample each group to at most n cells to speed up tests (default: NA)
# latent_vars: Additional variables to account for; multiple variables need to be concatenated by semicolons; will overwrite the default by param$latent_vars (default: none).
param$deg_contrasts = data.frame(condition_column=c("orig.ident", "orig.ident", "Phase"),
                                 condition_group1=c("pbmc_10x", "pbmc_10x", "G1"),
                                 condition_group2=c("pbmc_smartseq2_sample1", "pbmc_smartseq2_sample1", "G2M"),
                                 subset_column=c(NA, "seurat_clusters", "seurat_clusters"),
                                 subset_group=c(NA, "", "1;2"),
                                 downsample_cells_n=c(NA, 50, 30),
                                 log2FC=log2(c(1.5, 1.5, 1.5)))

# P-value threshold for functional enrichment tests
param$enrichr_padj = 0.05

# Enrichr databases of interest
param$enrichr_dbs = c("GO_Molecular_Function_2018", "GO_Biological_Process_2018", "GO_Cellular_Component_2018")

######################
# General parameters #
######################
# Main colour to use for plots
param$col = "palevioletred"

# Colour palette used for samples
param$col_palette_samples = "ggsci::pal_jama"

# Colour palette used for cluster
param$col_palette_clusters = "ggsci::pal_igv"

# Path to git repository
param$path_to_git = "."

# Debugging mode: 
# 'default_debugging' for default, 'terminal_debugger' for debugging without X11, 'print_traceback' for non-interactive sessions 
param$debugging_mode = "default_debugging"

# Git directory and files to source must be done first, then all helper functions can be sourced
git_files_to_source = c("functions_io.R",
                        "functions_plotting.R",
                        "functions_analysis.R",
                        "functions_degs.R",
                        "functions_util.R")
git_files_to_source = file.path(param$path_to_git, "R", git_files_to_source)
file_exists = purrr::map_lgl(git_files_to_source, file.exists)
if (any(!file_exists)) stop(paste("The following files could not be found:", paste(git_files_to_source[!file_exists], collapse=", "), ". Please check the git directory at '", param$path_to_git, "'.!"))
invisible(purrr::map(git_files_to_source, source))

# Debugging mode: 
# 'default_debugging' for default, 'terminal_debugger' for debugging without X11, 'print_traceback' for non-interactive sessions 
switch (param$debugging_mode, 
        default_debugging=on_error_default_debugging(), 
        terminal_debugger=on_error_start_terminal_debugger(),
        print_traceback=on_error_just_print_traceback(),
        on_error_default_debugging())

# Set output hooks
knitr::knit_hooks$set(message=format_message, warning=format_warning)

# Create output directories
if (!file.exists(param$path_out)) dir.create(param$path_out, recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "figures"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "marker_degs"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "annotation"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "data"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "export"), recursive=TRUE, showWarnings=FALSE)

# Path for figures in png and pdf format (trailing '/' is needed)
knitr::opts_chunk$set(fig.path=paste0(file.path(param$path_out, "figures"), "/"))

# Path for annotation
param$file_annot = file.path(param$path_out, "annotation", paste0(param$mart_dataset, ".v", param$annot_version, ".annot.txt"))

# Path for cell cycle genes file
param$file_cc_genes = file.path(param$path_out, "annotation", "cell_cycle_markers.xlsx")

# Do checks
error_messages = c()

# Check parameters and parse entries (e.g. numbers) so that they are valid
param = check_parameters_scrnaseq(param)
error_messages = c(error_messages, param[["error_messages"]])
# Check installed packages
error_messages = c(error_messages, check_installed_packages_scrnaseq())
# Check python
error_messages = c(error_messages, check_python())
# Check pandoc
error_messages = c(error_messages, check_pandoc())
# Check enrichR
error_messages = c(error_messages, check_enrichr(param$enrichr_dbs))
# Check ensembl
error_messages = c(error_messages, check_ensembl(biomart="ensembl", 
                                                 dataset=param$mart_dataset, 
                                                 mirror=param$biomart_mirror, 
                                                 version=param$annot_version,
                                                 attributes=param$mart_attributes,
                                                 file_annot=param$file_annot,
                                                 file_cc_markers=param$file_cc_genes))

# Stop here if there are errors
if (length(error_messages)) stop(paste(c("", paste("*", error_messages)), collapse="\n"))

Read data

Read and print mapping statistics

We begin by printing mapping statistics that have been produced prior to this workflow.

# Are statistics provided?
if (!all(is.na(param$path_data$stats))) { 
  
  # Loop through all samples and read mapping stats
  mapping_stats_list = list()
  for (i in 1:nrow(param$path_data)) {  
    if (!is.na(param$path_data$stats[i])) { 
      mapping_stats_list[[param$path_data$name[i]]] = read.delim(param$path_data$stats[i], 
                                                                 sep=",", header=FALSE, check.names=FALSE) %>%
        t() %>% as.data.frame()
    } 
  }
  
  # Join all mapping stats tables
  mapping_stats = mapping_stats_list %>% purrr::reduce(dplyr::full_join, by="V1")
  rownames(mapping_stats) = mapping_stats[["V1"]]
  mapping_stats = mapping_stats %>% dplyr::select(-V1)
  colnames(mapping_stats) = names(mapping_stats_list)
 
  # Print table to HTML 
  knitr::kable(mapping_stats, align="l", caption="Mapping statistics") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
 
} else { 
  message("Mapping statistics cannot be shown. No valid file provided.")
}

× (Message)
Mapping statistics cannot be shown. No valid file provided.

Read gene annotation

If not provided already, we read gene annotation from Ensembl and write the resulting table to file. We generate several dictionaries to translate between Ensembl IDs, gene symbols, Entrez Ids, and Seurat names.

# Read annotation from csv or from Ensembl and a tab separated txt will be created
if (file.exists(param$file_annot)) {
  annot_ensembl = read.delim(param$file_annot)
} else {
  annot_mart = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset=param$mart_dataset, 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  annot_ensembl = biomaRt::getBM(mart=annot_mart, attributes=param$mart_attributes, useCache=FALSE)
  write.table(annot_ensembl, file=param$file_annot, sep='\t', col.names=TRUE, row.names=FALSE, append=FALSE)
  message("Gene annotation file was created at: ", param$file_annot)
  # Note: depending on the attributes, there might be more than one row per gene
}

# Double-check if we got all required annotation, in case annotation file was read
check_annot_main = all(param$annot_main %in% colnames(annot_ensembl))
if (!check_annot_main) {
  stop("The annotation table misses at least one of the following columns: ", paste(param$annot_main, collapse=", "))
}

# Create translation tables
ensembl = param$annot_main["ensembl"]
symbol = param$annot_main["symbol"]
entrez = param$annot_main["entrez"]

# Ensembl id to gene symbol
ensembl_to_symbol = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_symbol = setNames(ensembl_to_symbol[, symbol], ensembl_to_symbol[, ensembl])

# Ensembl id to seurat-compatible unique rowname
ensembl_to_seurat_rowname = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_seurat_rowname[, symbol] = make.unique(gsub(pattern="_", replacement="-", x=ensembl_to_seurat_rowname[, symbol], fixed=TRUE))
ensembl_to_seurat_rowname = setNames(ensembl_to_seurat_rowname[, symbol], ensembl_to_seurat_rowname[, ensembl])

# Seurat-compatible unique rowname to ensembl id
seurat_rowname_to_ensembl = setNames(names(ensembl_to_seurat_rowname), ensembl_to_seurat_rowname)

# Ensembl to Entrez
ensembl_to_entrez = unique(annot_ensembl[, c(ensembl, entrez)])
ensembl_to_entrez[, entrez] = ifelse(nchar(ensembl_to_entrez[, entrez]) == 0, NA, ensembl_to_entrez[, entrez])
ensembl_to_entrez = split(ensembl_to_entrez[, entrez], ensembl_to_entrez[, ensembl])

# Seurat-compatible unique rowname to Entrez
seurat_rowname_to_ensembl_match = match(seurat_rowname_to_ensembl, names(ensembl_to_entrez))
names(seurat_rowname_to_ensembl_match) = names(seurat_rowname_to_ensembl)
seurat_rowname_to_entrez = purrr::map(seurat_rowname_to_ensembl_match, function(i) {unname(ensembl_to_entrez[[i]])})

# Entrez IDs is duplicating Ensembl IDs in annot_ensembl
# Therefore, we remove Entrez IDs from the annotation table, after generating all required translation tables
# Set rownames of annotation table to Ensembl identifiers
annot_ensembl = annot_ensembl[, -match(entrez, colnames(annot_ensembl))] %>% unique() %>% as.data.frame()
rownames(annot_ensembl) = annot_ensembl[, ensembl]

# Use biomart to translate human cell cycle genes to the species of interest and save them in a file
if (file.exists(param$file_cc_genes)) {
  # Load from file
  genes_s = openxlsx::read.xlsx(param$file_cc_genes, sheet=1)
  genes_g2m = openxlsx::read.xlsx(param$file_cc_genes, sheet=2)
  
} else { 
  # Obtain from Ensembl
  # Note: both mart objects must point to the same mirror for biomarT::getLDS to work
  mart_human = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset="hsapiens_gene_ensembl", 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  mart_myspecies = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                               dataset=param$mart_dataset, 
                                               mirror=GetBiomaRtMirror(mart_human), 
                                               version=param$annot_version)) 
  
  # S phase marker
  genes_s = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                          filters="external_gene_name", 
                          values=Seurat::cc.genes.updated.2019$s.genes, 
                          mart=mart_human, 
                          attributesL=c("ensembl_gene_id", "external_gene_name"), 
                          martL=mart_myspecies, 
                          uniqueRows=TRUE)
  colnames(genes_s) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # G2/M marker
  genes_g2m = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                            filters="external_gene_name", 
                            values=Seurat::cc.genes.updated.2019$g2m.genes, 
                            mart=mart_human, 
                            attributesL=c("ensembl_gene_id", "external_gene_name"), 
                            martL=mart_myspecies, 
                            uniqueRows=TRUE)
  colnames(genes_g2m) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # Write to file
  openxlsx::write.xlsx(list(S_phase=genes_s,G2M_phase=genes_g2m),file=param$file_cc_genes)
}

# Convert Ensembl ID to Seurat-compatible unique rowname
genes_s = data.frame(Human_gene_name=genes_s$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_s$Species_ensembl_id]))
genes_g2m = data.frame(Human_gene_name=genes_g2m$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_g2m$Species_ensembl_id]))

Read scRNA-seq data

We next read the scRNA-seq dataset(s) into Seurat.

# List of Seurat objects
sc = list()

datasets = param$path_data
for (i in seq(nrow(datasets))) {
  name = datasets[i, "name"]
  type = datasets[i, "type"]
  path = datasets[i, "path"]
  suffix = datasets[i, "suffix"]
  
  # Read 10X or smartseq2
  if (type == "10x") {
    # Read 10X sparse matrix into a Seurat object
    sc = c(sc, ReadSparseMatrix(path, project=name, row_name_column=1, convert_row_names=ensembl_to_seurat_rowname, cellnames_suffix=suffix))
    
  } else if (type == "smartseq2") {
    # Read counts table into a Seurat object
    sc = c(sc, ReadCountsTable(path, project=name, row_name_column=1, convert_row_names=ensembl_to_seurat_rowname, parse_plate_information=TRUE, return_samples_as_datasets=TRUE, cellnames_suffix=suffix))
  } 
}

# Make sure that sample names are unique. If not, just prefix with the dataset name. Also set orig.ident to this name.
sample_names = names(sc)
duplicated_sample_names_idx = which(sample_names %in% sample_names[duplicated(sample_names)])
for (i in duplicated_sample_names_idx) {
  sample_names[i] = paste(head(sc[[i]][["orig.dataset", drop=TRUE]], 1), sample_names[i], sep=".")
  sc[[i]][["orig.ident"]] = sample_names[i]
}

# Set up colors for samples and add them to the sc objects
sample_names = purrr::flatten_chr(purrr::map(sc, function(s) {
  nms = unique(as.character(s[[]][["orig.ident"]]))
  return(nms) 
}))
param$col_samples = GenerateColours(num_colours=length(sample_names), names=sample_names, palette=param$col_palette_samples, alphas=1)
sc = purrr::map(sc, ScAddLists, lists=list(orig.ident=param$col_samples), lists_slot="colour_lists")

# Downsample cells if requested
if (!is.null(param$downsample_cells_n)) {
  sc = purrr::map(sc, function(s) {
    cells = ScSampleCells(sc=s, n=param$downsample_cells_n, seed=1)
    return(subset(s, cells=cells))
  })
}

sc

## $pbmc_10x
## An object of class Seurat 
## 33694 features across 100 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 33694 features across 100 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)

The following first table shows metadata (columns) of the first 5 cells (rows). These metadata provide additional information about the cells in the dataset, such as the sample a cell belongs to (“orig.ident”), or the above mentioned number of unique genes detected (“nFeature_RNA”). The second table shows metadata (columns) of the first 5 genes (rows).

# Combine cell metadata of the Seurat objects into one big metadata
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s) return(s[[]])) %>% as.data.frame())

# Print cell metadata
knitr::kable(head(sc_cell_metadata), align="l", caption="Cell metadata, top 5 rows") %>%
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Cell metadata, top 5 rows

	orig.ident	nCount_RNA	nFeature_RNA	orig.dataset	SampleName	PlateNumber	PlateRow	PlateCol
PBMC1_10x_CACAGATGTAACAGTA-1	pbmc_10x	3538	1316	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_TTGACCCGTTCAGTAC-1	pbmc_10x	7333	2109	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AGTGCCGCAGGGAATC-1	pbmc_10x	5560	1915	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_GCATCTCTCCTGCTAC-1	pbmc_10x	8918	2801	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_CAAAGAAGTGACGTCC-1	pbmc_10x	4487	1426	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_CGGTCAGCACGGGTAA-1	pbmc_10x	4467	1513	pbmc_10x	NA	NA	NA	NA

# Print gene metadata
knitr::kable(head(sc[[1]][["RNA"]][[]], 5), align="l", caption="Feature metadata, top 5 rows (only first dataset shown)") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Feature metadata, top 5 rows (only first dataset shown)

	feature_id	feature_name	feature_type
TSPAN6	ENSG00000000003	TSPAN6	Gene Expression
TNMD	ENSG00000000005	TNMD	Gene Expression
DPM1	ENSG00000000419	DPM1	Gene Expression
SCYL3	ENSG00000000457	SCYL3	Gene Expression
C1orf112	ENSG00000000460	C1orf112	Gene Expression

Pre-processing

Quality control

We start the analysis by removing unwanted cells from the dataset(s). Three commonly used QC metrics include the number of unique genes detected in each cell (“nFeature_RNA”), the total number of molecules detected in each cell (“nCount_RNA”), and the percentage of counts that map to the mitochrondrial genome (“percent_mt”). If ERCC spike-in controls were used, the percentage of counts mapping to them is also shown (“percent_ercc”).

# If filters were specified globally (i.e. not by sample), this chunk will copy them for each sample such that downstream filtering can work by sample
param$cell_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$cell_filter)) {
    return(param$cell_filter[[s]])
  } else {
    return(param$cell_filter)
  }
})

param$feature_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$feature_filter)) {
    return(param$feature_filter[[s]])
  } else {
    return(param$feature_filter)
  }
})

# Calculate percentage of counts in mitochondrial genes for each Seurat object
sc = purrr::map(sc, function(s) {
  mt_features = grep(pattern=param$mt, rownames(s), value=TRUE)
  return(Seurat::PercentageFeatureSet(s, features=mt_features, col.name="percent_mt", assay="RNA"))
})

# Calculate percentage of counts in ribosomal genes for each Seurat object
sc = purrr::map(sc, function(s) {
  ribo_features = grep(pattern="^RP[SL]", rownames(s), value=TRUE, ignore.case=TRUE)
  return(Seurat::PercentageFeatureSet(s, features=ribo_features, col.name="percent_ribo", assay="RNA"))
})

# Calculate percentage of counts in ERCC for each Seurat object (if assay is available)
sc = purrr::map(sc, function(s) {
  if ("ERCC" %in% Seurat::Assays(s)) s$percent_ercc = s$nCount_ERCC/(s$nCount_ERCC + s$nCount_RNA)*100
  return(s)
  })

# Combine (again) cell metadata of the Seurat objects into one big metadata, this time including mt and ercc 
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ return(s[[]]) }) %>% as.data.frame())

# Only RNA assay at the moment
# counts_median uses sapply on the counts matrix, which converts the sparse matrix into a normal matrix
#   This might have to be adapted in future (Sparse Matrix Apply Function)
sc = purrr::map(list_names(sc), function(n) {
  # Calculate percentage of counts per gene in a cell
  counts_rna = Seurat::GetAssayData(sc[[n]], slot="counts", assay="RNA")
  total_counts = sc[[n]][["nCount_RNA", drop=TRUE]]
  counts_rna_perc = Matrix::t(Matrix::t(counts_rna)/total_counts)*100

  # Calculate feature filters
  num_cells_expr = Matrix::rowSums(counts_rna >= 1)
  num_cells_expr_threshold = Matrix::rowSums(counts_rna >= param$feature_filter[[n]][["min_counts"]])
  
  # Calculate median of counts_rna_perc per gene 
  counts_median = apply(counts_rna_perc, 1, median)
  
  # Add all QC measures as metadata
  sc[[n]][["RNA"]] = Seurat::AddMetaData(sc[[n]][["RNA"]], data.frame(num_cells_expr, num_cells_expr_threshold, counts_median))
  return(sc[[n]])
})

# Plot QC metrics for cells
cell_qc_features = c("nFeature_RNA", "nCount_RNA", "percent_mt")
if ("percent_ercc" %in% colnames(sc_cell_metadata)) cell_qc_features = c(cell_qc_features, "percent_ercc")
cell_qc_features = values_to_names(cell_qc_features)

p_list = list()
for (i in names(cell_qc_features)) {
  p_list[[i]]= ggplot(sc_cell_metadata[, c("orig.ident", i)], aes_string(x="orig.ident", y=i, fill="orig.ident", group="orig.ident")) +
    geom_violin(scale="width")

  # Adds points for samples with less than three cells since geom_violin does not work here
  p_list[[i]] = p_list[[i]] + 
    geom_point(data=sc_cell_metadata[, c("orig.ident", i)] %>% dplyr::filter(orig.ident %in% names(which(table(sc_cell_metadata$orig.ident) < 3))), aes_string(x="orig.ident", y=i, fill="orig.ident"), shape=21, size=2)
  
  # Now add styles
  p_list[[i]] = p_list[[i]] + 
    AddStyle(title=i, legend_position="none", fill=param$col_samples, xlab="") + 
    theme(axis.text.x=element_text(angle=45, hjust=1))
  
  # Creates a table with min/max values for filter i for each dataset
  cell_filter_for_plot = purrr::map_dfr(names(param$cell_filter), function(n) {
    # If filter i in cell filter of the dataset, then create dataframe with columns orig.ident, threshold and value
    if (i %in% names(param$cell_filter[[n]])){
      data.frame(orig.ident=n, threshold=c("min", "max"), value=param$cell_filter[[n]][[i]], stringsAsFactors=FALSE)
    } 
  })
  
  # Add filters as segments to plot
  if (nrow(cell_filter_for_plot) > 0) {
    # Remove entries that are NA
    cell_filter_for_plot = cell_filter_for_plot %>% dplyr::filter(!is.na(value))
    p_list[[i]] = p_list[[i]] + geom_segment(data=cell_filter_for_plot, 
                                             aes(x=as.integer(as.factor(orig.ident))-0.5, 
                                                 xend=as.integer(as.factor(orig.ident))+0.5, 
                                                 y=value, yend=value), 
                                             lty=2, col="firebrick")
  }
}
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Distribution of feature values") 
p

# Correlate QC metrics for cells
p_list = list()
p_list[[1]] = ggplot(sc_cell_metadata, aes_string(x=cell_qc_features[2], y=cell_qc_features[1], colour="orig.ident")) +
  geom_point() + 
  AddStyle(col=param$col_samples)
p_list[[2]] = ggplot(sc_cell_metadata, aes_string(x=cell_qc_features[3], y=cell_qc_features[1], colour="orig.ident")) +
  geom_point() + 
  AddStyle(col=param$col_samples)
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Features plotted against each other")

if (length(sc)==1) {
  p = p & theme(legend.position="bottom")
} else {
  p = p + patchwork::plot_layout(guides="collect") & theme(legend.position="bottom")
}
p

Genes with highest expression

We next investigate whether there are individual genes that are represented by an unusually high number of counts. For each cell, we first calculate the percentage of counts per gene. Subsequently, for each gene, we calculate the median value of these percentages. Genes with the highest median percentage of counts are plotted below.

# Plot only samples that we intend to keep 
sc_names = names(sc)[!(names(sc) %in% param$samples_to_drop)]
genes_highestExpr = lapply(sc_names, function(i) {
  top_ten_exp = sc[[i]][["RNA"]][["counts_median"]] %>% dplyr::arrange(dplyr::desc(counts_median)) %>% head(n=10)
  return(rownames(top_ten_exp))
  }) %>%
  unlist() %>%
  unique()

genes_highestExpr_counts = purrr::map_dfc(sc[sc_names], .f=function(s) s[["RNA"]][["counts_median"]][genes_highestExpr, ]) 
genes_highestExpr_counts$gene = genes_highestExpr
genes_highestExpr_counts = genes_highestExpr_counts %>% tidyr::pivot_longer(cols=all_of(sc_names))
genes_highestExpr_counts$name = factor(genes_highestExpr_counts$name, levels=sc_names)

col =  GenerateColours(num_colours=length(genes_highestExpr), names=genes_highestExpr, palette="ggsci::pal_simpsons")
p = ggplot(genes_highestExpr_counts, aes(x=name, y=value, col=gene, group=gene)) + 
  geom_point() + 
  AddStyle(title="Top 10 highest expressed genes per sample, added into one list", 
           xlab="Sample", ylab="Median % of raw counts\n per gene in a cell", 
           legend_position="bottom", 
           col=col)
if (length(unique(genes_highestExpr_counts$name))>1) p = p + geom_line()
p

Filtering

Cells and genes are filtered based on the following thresholds:

cell_filter_lst = param$cell_filter %>% unlist(recursive=FALSE)
is_numeric_filter = purrr::map_lgl(cell_filter_lst, function(f) return(is.numeric(f) & length(f)==2))

# numeric cell filters
if (length(cell_filter_lst[is_numeric_filter]) > 0) {
  purrr::invoke(rbind, cell_filter_lst[is_numeric_filter]) %>%
    knitr::kable(align="l", caption="Numeric filters applied to cells", col.names=c("Min", "Max")) %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}

Numeric filters applied to cells

	Min	Max
pbmc_10x.nFeature_RNA	200	NA
pbmc_10x.percent_mt	NA	20
pbmc_smartseq2_sample1.nFeature_RNA	200	NA
pbmc_smartseq2_sample1.percent_mt	NA	20

# categorial cell filters
if (length(cell_filter_lst[!is_numeric_filter]) > 0) {
purrr::invoke(rbind, cell_filter_lst[!is_numeric_filter] %>% purrr::map(paste, collapse=",")) %>%
  knitr::kable(align="l", caption="Categorial filters applied to cells", col.names=c("Values")) %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}
  
# gene filters
feature_filter_lst = param$feature_filter %>% unlist(recursive=FALSE)
if (length(feature_filter_lst) > 0) {
  purrr::invoke(rbind, feature_filter_lst) %>% 
    knitr::kable(align="l", caption="Filters applied to genes", col.names=c("Value")) %>%
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}

Filters applied to genes

	Value
pbmc_10x.min_counts	1
pbmc_10x.min_cells	3
pbmc_smartseq2_sample1.min_counts	1
pbmc_smartseq2_sample1.min_cells	3

The number of excluded cells and features is as follows:

# Iterate over datasets and filters
# Record a cell if it does not pass the filter
# Also record a cell if it belongs to a sample that should be dropped
sc_cells_to_exclude  = purrr::map(list_names(sc), function(n) { 
  filter_result = purrr::map(list_names(param$cell_filter[[n]]), function(f) {
    filter = param$cell_filter[[n]][[f]]
    if (is.numeric(filter)) {
      if (is.na(filter[1])) filter[1] = -Inf # Minimum
      if (is.na(filter[2])) filter[2] = Inf  # Maximum 
      idx_exclude = sc[[n]][[f, drop=TRUE]] < filter[1] | sc[[n]][[f, drop=TRUE]] > filter[2]
      return(names(which(idx_exclude)))
    } else if (is.character(filter)) { 
      idx_exclude = !sc[[n]][[f, drop=TRUE]] %in% filter
      return(Cells(sc[[n]])[idx_exclude])
    }
  })

  # Samples to drop
  if (n %in% param$samples_to_drop) {
    filter_result[["samples_to_drop"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_to_drop"]] = as.character(c())
  }
  
  # Minimum number of cells for a sample to keep
  if (ncol(sc[[n]]) < param$samples_min_cells) {
    filter_result[["samples_min_cells"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_min_cells"]] = as.character(c())
  }
  
  return(filter_result)
})

# Summarise
sc_cells_to_exclude_summary = purrr::map_dfr(sc_cells_to_exclude, function(s) {
  return(as.data.frame(purrr::map(s, length))) 
  })
rownames(sc_cells_to_exclude_summary) = names(sc_cells_to_exclude)
knitr::kable(sc_cells_to_exclude_summary, align="l", caption="Number of excluded cells") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) 

Number of excluded cells

	nFeature_RNA	percent_mt	samples_to_drop	samples_min_cells
pbmc_10x	8	9	0	0
pbmc_smartseq2_sample1	0	0	0	0

# Now filter, drop the respective colours and adjust integration method
sc = purrr::map(list_names(sc), function(n) {
  cells_to_keep = Cells(sc[[n]])
  cells_to_keep = cells_to_keep[!cells_to_keep %in% purrr::flatten_chr(sc_cells_to_exclude[[n]])]
  if (length(cells_to_keep)==0) return(NULL)
  else return(subset(sc[[n]], cells=cells_to_keep))
}) %>% purrr::discard(is.null)

if (length(sc)==1) param$integrate_samples[["method"]] = "single"

# Only RNA assay at the moment

# Iterate over datasets and record a feature if it does not pass the filter
sc_features_to_exclude = purrr::map(list_names(sc), function(n) {
  if (length(Cells(sc[[n]])) < param$feature_filter[[n]][["min_cells"]]) return(list())
  else return(names(which(sc[[n]][["RNA"]][["num_cells_expr_threshold", drop=TRUE]] < param$feature_filter[[n]][["min_cells"]])))
})

# Summarise
sc_features_to_exclude_summary = purrr::map(sc_features_to_exclude, length) %>% 
  t() %>% as.data.frame() 
rownames(sc_features_to_exclude_summary) = c("Genes")
knitr::kable(sc_features_to_exclude_summary, align="l", caption="Number of excluded genes") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))

Number of excluded genes

	pbmc_10x	pbmc_smartseq2_sample1
Genes	23929	19906

# Now filter
sc = purrr::map(list_names(sc), function(n) {
  assay_names = Seurat::Assays(sc[[n]])
  features_to_keep = purrr::map(values_to_names(assay_names), function(a) {
    features = rownames(sc[[n]][[a]])
    keep = features[!features %in% sc_features_to_exclude[[n]]]
    return(keep)
  })
  return(subset(sc[[n]], features=purrr::flatten_chr(features_to_keep)))
})

After filtering, the size of the Seurat object is:

sc

## $pbmc_10x
## An object of class Seurat 
## 9765 features across 89 samples within 1 assay 
## Active assay: RNA (9765 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 13788 features across 100 samples within 1 assay 
## Active assay: RNA (13788 features, 0 variable features)

Normalisation

In this section, we subsequently run a series of Seurat functions for each provided sample:
1. We start by running a standard log normalisation, where counts for each cell are divided by the total counts for that cell and multiplied by 10,000. This is then natural-log transformed.
2. We assign cell cycle scores to each cell based on its normalised expression of G2/M and S phase markers. These scores are visualised in a separate section further below. If specified in the above parameter section, cell cycle effects are removed during scaling (step 3).
3. Dependent on the normalisation of your choice, we either
3a. Run standard functions to select variable genes, and scale normalised gene counts. For downstream analysis it is beneficial to focus on genes that exhibit high cell-to-cell variation, that is they are highly expressed in some cells and lowly in others. To be able to compare normalised gene counts between genes, gene counts are further scaled to have zero mean and unit variance (z-score).
3b. Run SCTransform, a new and more sophisticated normalisation method that replaces the previous functions (normalisation, variable genes and scaling).

Note that removing all signal associated to cell cycle can negatively impact downstream analysis. For example, in differentiating processes, stem cells are quiescent and differentiated cells are proliferating (or vice versa), and removing all cell cycle effects can blur the distinction between these cells. As an alternative, we can remove the difference between G2M and S phase scores. This way, signals separating non-cycling and cycling cells will be maintained, while differences amongst proliferating cells will be removed. For a more detailed explanation, see the cell cycle vignette for Seurat https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html. Cell cycle effects removed for this report: FALSE; all cell cycle effects removed for this report: FALSE.

While raw data is typically used for statistical tests such as finding marker genes, normalised data is mainly used for visualising gene expression values. Scaled data include variable genes only, potentially without cell cycle effects, and are mainly used to determine the structure of the dataset(s) with Principal Component Analysis, and indirectly to cluster and visualise cells in 2D space.

# Normalise data the original way
#   This is required to score cell cycle (https://github.com/satijalab/seurat/issues/1679)
sc = purrr::map(sc, Seurat::NormalizeData, normalization.method="LogNormalize", scale.factor=10000, verbose=FALSE)

# Determine cell cycle effect per sample 
sc = purrr::map(list_names(sc), function(n) {
  sc[[n]] = CCScoring(sc=sc[[n]], genes_s=genes_s[,2], genes_g2m=genes_g2m[,2], name=n)
  if (any(is.na(sc[[n]][["S.Score"]])) | any(is.na(sc[[n]][["G2M.Score"]]))) {
    param$cc_remove=FALSE
    param$cc_remove_all=FALSE
    param$cc_rescore_after_merge=FALSE
  }
  return(sc[[n]])
})

# If cell cycle effects should be removed, we first score cells 
# The effect is then removed in the following chunk 
if (param$cc_remove) {
# Add to vars that need to regressed out during normalisation
  if (param$cc_remove_all) {
    # Remove all signal associated to cell cycle
    param$vars_to_regress = unique(c(param$vars_to_regress, "S.Score", "G2M.Score"))
    param$latent_vars = unique(c(param$latent_vars, "S.Score", "G2M.Score"))
  } else {
    # Don't remove the difference between cycling and non-cycling cells 
    param$vars_to_regress = unique(c(param$vars_to_regress, "CC.Difference"))
    param$latent_vars = unique(c(param$latent_vars, "CC.Difference"))
  }  
}

if (param$norm == "RNA") { 
  # Find variable features from normalised data (unaffected by scaling)
  sc = purrr::map(sc, Seurat::FindVariableFeatures, selection.method="vst", nfeatures=3000, verbose=FALSE)
  
  # Scale 
  # Note: For a single dataset where no integration/merging is needed, all features can already be scaled here. 
  #   Otherwise, scaling of all features will be done after integration/merging.
  if (param$integrate_samples[["method"]]=="single") {
    sc[[1]] = Seurat::ScaleData(sc[[1]], 
                      features=rownames(sc[[1]][["RNA"]]),
                      vars.to.regress=param$vars_to_regress, 
                      verbose=FALSE) 
  }
} else if (param$norm == "SCT") {
  # Run SCTransform
  #
  # This is a new normalisation method that replaces previous Seurat functions 'NormalizeData', 'FindVariableFeatures', and 'ScaleData'. 
  # vignette: https://satijalab.org/seurat/v3.0/sctransform_vignette.html
  # paper: https://www.biorxiv.org/content/10.1101/576827v2
  # Normalised data end up here: sc@assays$SCT@data
  # Note: For a single dataset where no integration is needed, all features can already be scaled here. 
  #   Otherwise, it is enough to scale only the variable features.
  # Note: It is not guaranteed that all genes are successfully normalised with SCTransform. 
  #   Consequently, some genes might be missing from the SCT assay. 
  #   See: https://github.com/ChristophH/sctransform/issues/27
  # Note: The performance of SCTransform can be improved by using 'glmGamPoi' instead of 'poisson' as method for initial parameter estimation.
  sc = purrr::map(list_names(sc), function(n) { 
    SCTransform(sc[[n]], 
                vars.to.regress=param$vars_to_regress, 
                min_cells=param$feature_filter[[n]][["min_cells"]], 
                verbose=FALSE, 
                return.only.var.genes=!(param$integrate_samples[["method"]]=="single"),
                method=ifelse(packages_installed("glmGamPoi"), "glmGamPoi", "poisson")) 
  })
}

Variable genes

Experience shows that 1,000-2,000 genes with the highest cell-to-cell variation are often sufficient to describe the global structure of a single cell dataset. For example, cell type-specific genes typically highly vary between cells. Housekeeping genes, on the other hand, are similarly expressed across cells and can be disregarded to differentiate between cells.

To determine variable genes, we need to separate biological variability from technical variability. Technical variability arises especially for lowly expressed genes, where high variability corresponds to small absolute changes that we are not interested in. Here, we use the variance-stabilizing transformation (vst) method implemented in Seurat (Hafemeister and Satija (2019)). This method first models the technical variability as a relationship between mean gene expression and variance using local polynomial regression. The model is then used to calculate the expected variance based on the observed mean gene expression. The difference between the observed and expected variance is called residual variance and likely reflects biological variability. The top 3,000 variable genes are used for further analysis.

fig_height_vf = 5 * ceiling(length(names(sc))/2)

p_list = purrr::map(list_names(sc), function(n) {
  top10 = head(Seurat::VariableFeatures(sc[[n]], assay=param$norm), 10)
  p = Seurat::VariableFeaturePlot(sc[[n]], 
                                  assay=param$norm, 
                                  selection.method=ifelse(param$norm=="RNA", "vst", "sct"), 
                                  col=c("grey", param$col)) + 
    AddStyle(title=n) + 
    theme(legend.position=c(0.2, 0.8), legend.background=element_rect(fill=alpha("white", 0.0)))
  p = LabelPoints(plot=p, points=top10, repel=TRUE, xnudge=0, ynudge=0)
  return(p)
})

p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Variable genes")
p

Combining multiple samples

if (param$integrate_samples[["method"]]!="single") {
  
  # When merging, feature meta-data is removed by Seurat entirely; save separately for each assay except for SCT and add again afterwards
  # Note: not sure whether this is still needed - discuss
  assay_names = setdiff(unique(purrr::flatten_chr(purrr::map(list_names(sc), function(n) { Seurat::Assays(sc[[n]]) } ))), "SCT")

  # Loop through all assays and accumulate meta data
  sc_feature_metadata = purrr::map(values_to_names(assay_names), function(a) {
    # "feature_id", "feature_name", "feature_type" are accumulated for all assays and stored just once
    # This step is skipped for assays that do not contain all three types of feature information
    contains_neccessary_columns = purrr::map_lgl(list_names(sc), function(n) { 
      all(c("feature_id", "feature_name", "feature_type") %in% colnames(sc[[n]][[a]][[]])) 
      })

    if (all(contains_neccessary_columns)) {
      feature_id_name_type = purrr::map(sc, function(s) return(s[[a]][[c("feature_id", "feature_name", "feature_type")]]) )
      feature_id_name_type = purrr::reduce(feature_id_name_type, function(df_x, df_y) {
        new_rows = which(!rownames(df_y) %in% rownames(df_x))
        if (length(new_rows) > 0) return(rbind(df_x, df_y[new_rows, ]))
        else return(df_x)
      })
      feature_id_name_type$row_names = rownames(feature_id_name_type)
    } else {
      feature_id_name_type = NULL
    }
    
    # For all other meta-data, we prefix column names with the dataset
    other_feature_data = purrr::map(list_names(sc), function(n) {
      df = sc[[n]][[a]][[]]
      if (contains_neccessary_columns[[n]]) df = df %>% dplyr::select(-dplyr::one_of(c("feature_id", "feature_name", "feature_type"), c()))
      if (ncol(df) > 0) colnames(df) = paste(n, colnames(df), sep=".")
      df$row_names = rownames(df)
      return(df)
    })
    
    # Now join everything by row_names by full outer join
    if (!is.null(feature_id_name_type)) {
      feature_data = purrr::reduce(c(list(feature_id_name_type=feature_id_name_type), other_feature_data), dplyr::full_join, by="row_names")
    } else {
      feature_data = purrr::reduce(other_feature_data, dplyr::full_join, by="row_names")
    }
    rownames(feature_data) = feature_data$row_names
    feature_data$row_names = NULL
    
    return(feature_data)
  })
  
  # When merging, cell meta-data are merged but factors are not kept
  sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ s[[]] }) %>% as.data.frame())
  sc_cell_metadata_factor_levels = purrr::map(which(sapply(sc_cell_metadata, is.factor)), function(n) {
    return(levels(sc_cell_metadata[, n, drop=TRUE]))
  })
}

# Standard method for integrating multiple samples. Best performance but computationally intensive.
if (param$integrate_samples[["method"]]=="integrate") {
  if (!"use_reciprocal_pca" %in% names(param$integrate_samples)) param$integrate_samples[["use_reciprocal_pca"]] = FALSE

  # Run integration
  sc = RunIntegration(sc, 
                      assay=param$norm,
                      ndims=param$integrate_samples[["dimensions"]], 
                      verbose=FALSE, 
                      reference=param$integrate_samples[["reference"]], 
                      use_reciprocal_pca=param$integrate_samples[["use_reciprocal_pca"]], 
                      k_filter=param$integrate_samples[["k.filter"]], 
                      k_weight=param$integrate_samples[["k.weight"]], 
                      k_anchor=param$integrate_samples[["k.anchor"]],
                      k_score=param$integrate_samples[["k.score"]])

  # Re-score cell cycle effects after integration
  if (param$cc_rescore_after_merge) {
    sc = CCScoring(sc=sc, genes_s=genes_s[,2], genes_g2m=genes_g2m[,2])
    if (any(is.na(sc[["S.Score"]])) | any(is.na(sc[["G2M.Score"]])))  {
      param$cc_remove=FALSE
      param$cc_remove_all=FALSE
      param$cc_rescore_after_merge=FALSE
      param$vars_to_regress = setdiff(param$vars_to_regress, c("S.Score", "G2M.Score", "CC.Difference"))
      param$latent_vars = setdiff(param$latent_vars, c("S.Score", "G2M.Score", "CC.Difference"))
    }
  }
  
  # (Re-)Run scaling
  if (param$norm == "RNA") {
    # According to Seurat, we need to scale data again for "RNAintegrated", and "RNA"
    DefaultAssay(sc) = "RNAintegrated"
    sc = Seurat::ScaleData(sc, 
                                features=rownames(sc[["RNAintegrated"]]), 
                                vars.to.regress=param$vars_to_regress, 
                                assay="RNAintegrated",
                                verbose=FALSE)
      
    DefaultAssay(sc) = "RNA"
    sc = Seurat::ScaleData(sc, 
                                features=rownames(sc[["RNA"]]), 
                                vars.to.regress=param$vars_to_regress, 
                                assay="RNA",
                                verbose=FALSE)
  } else if (param$norm == "SCT") {
    # We need to re-run SCTransform for the "SCT" assay again, to normalise on the complete dataset
    DefaultAssay(sc) = "SCT"
    min_cells_overall = max(purrr::map_int(param$feature_filter, function(f) as.integer(f[["min_cells"]])))
    sc = SCTransform(sc, vars.to.regress=param$vars_to_regress, 
                     min_cells=min_cells_overall,
                     verbose=FALSE,
                     return.only.var.genes=FALSE,
                     method=ifelse(packages_installed("glmGamPoi"), "glmGamPoi", "poisson"))
  }
  
  # Add feature metadata
  for (a in Seurat::Assays(sc)) {
    if (a %in% names(sc_feature_metadata)) {
      sc[[a]] = Seurat::AddMetaData(sc[[a]], sc_feature_metadata[[a]][rownames(sc[[a]]),, drop=FALSE])
    }
  }
  
  # Fix cell metadata factors
  for (f in names(sc_cell_metadata_factor_levels)) {
    sc[[f]] = factor(sc[[f, drop=TRUE]], levels=sc_cell_metadata_factor_levels[[f]])
  }
  
  # Add sample colours again to misc slot
  sc = ScAddLists(sc, lists=list(orig.ident=param$col_samples), lists_slot="colour_lists")

  # Set default assay (will be the integrated version)
  DefaultAssay(sc) = paste0(param$norm, "integrated")  
  
  message("Data values for all samples have been integrated.")
  print(sc)
}

× (Message)
Data values for all samples have been integrated.

## An object of class Seurat 
## 17388 features across 189 samples within 2 assays 
## Active assay: RNAintegrated (3000 features, 3000 variable features)
##  1 other assay present: RNA

Relative log expression

n_cells_rle_plot = 100

# Sample at most 100 cells per dataset and save their identity
cells_subset = sc[["orig.ident"]] %>% tibble::rownames_to_column() %>% 
  dplyr::group_by(orig.ident) %>% 
  dplyr::sample_n(size=min(n_cells_rle_plot, length(orig.ident))) %>% 
  dplyr::select(rowname, orig.ident)

To better understand the efficiency of the applied normalisation procedures, we plot the relative log expression of genes in at most 100 randomly selected cells per sample before and after normalisation. This type of plot reveals unwanted variation in your data. The concept is taken from Gandolfo and Speed (2018). In brief, we remove variation between genes, leaving only variation between samples. If expression levels of most genes are similar in all cell types, sample heterogeneity is a sign of unwanted variation.

For each gene, we calculate its median expression across all cells, and then calculate the deviation from this median for each cell. For each cell, we plot the median expression (black), the interquartile range (lightgrey), whiskers defined as 1.5 times the interquartile range (darkgrey), and outliers (#374E55FF, #DF8F44FF)

Raw counts

# Plot raw data
p = PlotRLE(as.matrix(log2(GetAssayData(subset(sc, cells=cells_subset$rowname), assay="RNA", slot="counts") + 1)), 
            id=cells_subset$orig.ident, 
            col=param$col_samples) + 
  labs(title="log2(raw counts + 1)")
p

Normalised data

Dependent on the context, this tab refers to different data:

• Single sample: RNA normalisation of the single sample
  
• Multiple samples that were merged: Combined RNA normalisation post merging of all samples
  
• Multiple samples that were integrated: Separate RNA normalisation prior to integration of all samples

# Plot normalised data
p = PlotRLE(as.matrix(GetAssayData(subset(sc, cells=cells_subset$rowname), assay=param$norm, slot="data")), 
            id=cells_subset$orig.ident, 
            col=param$col_samples) + 
  labs(title="Normalised data")
p

Integrated data

if (! param$integrate_samples[["method"]] %in% c("single", "merge")) {
  # Plot integrated data
  p = PlotRLE(as.matrix(GetAssayData(subset(sc, cells=cells_subset$rowname), assay=paste0(param$norm, "integrated"), slot="data")), 
              id=cells_subset$orig.ident, 
              col=param$col_samples) + 
    labs(title="Integrated data")
  p
} else {
  message("No integrated data available.")
}

Dimensionality reduction

A single-cell dataset of 20,000 genes and 5,000 cells has 20,000 dimensions. At this point of the analysis, we have already reduced the dimensionality of the dataset to 3,000 variable genes. The biological manifold however can be described by far fewer dimensions than the number of (variable) genes. Dimension reduction methods aim to find these dimensions. There are two general purposes for dimension reduction methods: to summarise a dataset, and to visualise a dataset.

We use Principal Component Analysis (PCA) to summarise a dataset, overcoming noise and reducing the data to its essential components. Each principal component (PC) represents a “metafeature” that combines information across a correlated gene set. Later, we use Uniform Manifold Approximation and Projection (UMAP) to visualise the dataset, placing similar cells together in 2D space, see below.

To decide how many PCs to include in downstream analyses, we visualise the cells and genes that define the PCA.

# Run PCA for default normalisation
sc = Seurat::RunPCA(sc, features=Seurat::VariableFeatures(object=sc), verbose=FALSE, npcs=min(50, ncol(sc)))

p_list = Seurat::VizDimLoadings(sc, dims=1:2, reduction="pca", col=param$col, combine=FALSE, balanced=TRUE)
for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
p =  patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Top gene loadings of the first two PCs") 
p

p = Seurat::DimPlot(sc, reduction="pca", cols=param$col_samples) + 
  AddStyle(title="Cells arranged by the first two PCs", legend_position="bottom")
p

p_list = Seurat::DimHeatmap(sc, dims=1:min(20, ncol(sc)), cells=min(500, ncol(sc)), balanced=TRUE, fast=FALSE, combine=FALSE)
p_list = purrr::map(seq(p_list), function(i) {
  p_list[[i]] = p_list[[i]] + 
    ggtitle(paste("PC", i)) + 
    theme(legend.position="none", axis.text.y=element_text(size=8))
  return(p_list[[i]])
  })
p = patchwork::wrap_plots(p_list, ncol=3) + patchwork::plot_annotation("Top gene loadings of the first PCs")
p

Dimensionality of the dataset

We next need to decide how many PCs we want to use for our analyses. The following “Elbow plot” is designed to help us make an informed decision. PCs are ranked based on the percentage of variance they explain.

# More approximate technique used to reduce computation time
p = Seurat::ElbowPlot(sc, ndims=min(20, ncol(sc))) + 
  geom_vline(xintercept=param$pc_n + .5, col="firebrick", lty=2) + 
  AddStyle(title="Elbow plot") 
p

# Cannot have more PCs than number of cells
param$pc_n = min(param$pc_n, ncol(sc))

For the current dataset, 10 PCs were chosen.

Clustering

Seurat’s clustering method first constructs a graph structure, where nodes are cells and edges are drawn between cells with similar gene expression patterns. Technically speaking, Seurat first constructs a K-nearest neighbor (KNN) graph based on Euclidean distance in PCA space, and refines edge weights between cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To partition the graph into highly interconnected parts, cells are iteratively grouped together using the Leiden algorithm (Traag, Waltman, and van Eck 2019).

# Construct phylogenetic tree relating the 'average' cell from each sample
if (length(levels(sc$orig.ident)) > 1) {
  sc = suppressWarnings(Seurat::BuildClusterTree(sc, features=rownames(sc), verbose=FALSE))
  Seurat::Misc(sc, "trees") = list(orig.ident = Seurat::Tool(sc, "Seurat::BuildClusterTree"))
}

# The number of clusters can be optimized by tuning 'resolution' -> based on feedback from the client whether or not clusters make sense
# Choose the number of PCs to use for clustering
sc = Seurat::FindNeighbors(sc, dims=1:param$pc_n, verbose=FALSE)

# Seurat vignette suggests resolution parameter between 0.4-1.2 for datasets of about 3k cells
sc = Seurat::FindClusters(sc, resolution=param$cluster_resolution, algorithm=4, verbose=FALSE, method="igraph")

# Construct phylogenetic tree relating the 'average' cell from each cluster
if (length(levels(sc$seurat_clusters)) > 1) {
  sc = suppressWarnings(Seurat::BuildClusterTree(sc, dims=1:param$pc_n, verbose=FALSE))
  suppressWarnings({Seurat::Misc(sc, "trees") = c(Seurat::Misc(sc, "trees"), list(seurat_clusters = Seurat::Tool(sc, "Seurat::BuildClusterTree")))})
}

# Set up colors for clusters and add the sc object
param$col_clusters = GenerateColours(num_colours=length(levels(sc$seurat_clusters)), names=levels(sc$seurat_clusters), 
                                     palette=param$col_palette_clusters, alphas=1)
sc = ScAddLists(sc, lists=list(seurat_clusters=param$col_clusters), lists_slot="colour_lists")

Visualisation with UMAP

We use a UMAP to visualise and explore a dataset. The goal is to place similar cells together in 2D space, and learn about the biology underlying the data. Cells are color-coded according to the graph-based clustering, and clusters typcially co-localise on the UMAP.

Take care not to mis-read a UMAP:

• Parameters influence the plot (we use defaults here)
  
• Cluster sizes relative to each other mean nothing, since the method has a local notion of distance
  
• Distances between clusters might not mean anything
  
• You may need more than one plot

For a nice read to intuitively understand UMAP, see https://pair-code.github.io/understanding-umap/.

Coloured by cluster

# Default UMAP
sc = suppressWarnings(Seurat::RunUMAP(sc, dims=1:param$pc_n, verbose=FALSE, umap.method="uwot"))

# Note that you can set `label = TRUE` or use the LabelClusters function to help label individual clusters
cluster_cells = table(sc@active.ident)
cluster_labels = paste0(levels(sc@active.ident)," (", cluster_cells[levels(sc@active.ident)],")")
p = Seurat::DimPlot(sc, reduction="umap", group.by="seurat_clusters") + 
  scale_color_manual(values=param$col_clusters, labels=cluster_labels) +
  AddStyle(title="UMAP, cells coloured by cluster identity", legend_position="bottom", legend_title="Clusters")
p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
p

Coloured by sample

# Add a UMAP that is coloured by sample of origin
cell_samples = sc[[]] %>% dplyr::pull(orig.ident) %>% unique() %>% sort()

# Note: This is a hack to colour by sample but label by Cluster
p = Seurat::DimPlot(sc, reduction="umap", group.by="orig.ident", cols=param$col_samples) +
  AddStyle(title="UMAP, cells coloured by sample of origin", legend_position="bottom")
p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
p

Distribution of cells in clusters

# Count cells per cluster per sample 
cell_samples = sc[[]] %>% dplyr::pull(orig.ident) %>% levels()
cell_clusters = sc[[]] %>% dplyr::pull(seurat_clusters) %>% levels()

tbl = dplyr::count(sc[[c("orig.ident", "seurat_clusters")]], orig.ident, seurat_clusters) %>% tidyr::pivot_wider(names_from="seurat_clusters", names_prefix="Cl_", values_from=n) %>% as.data.frame()
rownames(tbl) = paste0(tbl[,"orig.ident"],"_n")
tbl[,"orig.ident"] = NULL

# Add percentages
tbl_perc = round(t(tbl) / colSums(tbl) * 100, 2) %>% t()
rownames(tbl_perc) = gsub(rownames(tbl_perc), pattern="_n$", replacement="_perc", perl=TRUE)
tbl = rbind(tbl, tbl_perc)

# Add enrichment
if (length(cell_samples) > 1 & length(cell_clusters) > 1) tbl = rbind(tbl, CellsFisher(sc))

# Sort
tbl = tbl[order(rownames(tbl)),, drop=FALSE]

# Plot percentages
tbl_bar = tbl[paste0(cell_samples, "_perc"), , drop=FALSE] %>% 
  tibble::rownames_to_column(var="Sample") %>%
  tidyr::pivot_longer(tidyr::starts_with("Cl"), names_to="Cluster", values_to="Percentage")
tbl_bar$Cluster = tbl_bar$Cluster %>% gsub(pattern="^Cl_", replacement="", perl=TRUE) %>% factor(levels=sc$seurat_clusters %>% levels())
tbl_bar$Sample = tbl_bar$Sample %>% gsub(pattern="_perc$", replacement="", perl=TRUE) %>% as.factor()
tbl_bar$Percentage = as.numeric(tbl_bar$Percentage)
p = ggplot(tbl_bar, aes(x=Cluster, y=Percentage, fill=Sample)) + 
  geom_bar(stat="identity" ) +
  AddStyle(title="Percentage cells of samples in clusters",
           fill=param$col_samples,
           legend_title="Sample",
           legend_position="bottom")
p

The following table shows the number of cells per sample per cluster:

• n: Number of cells per sample per cluster
  
• perc: Percentage of cells per sample per cluster compared to all other cells of that cluster

In case the dataset contains 2 or more samples, we also calculate whether or not the number of cells of a sample in a cluster is significantly higher or lower than expected:

• oddsRatio: Odds ratio calculated for cluster c1 and sample s1 as (# cells s1 in c1 / # cells not s1 in c1) / (# cells s1 not in c1 / # cells not s1 not in c1)
  
• p: P-value calculated with a Fisher test to test whether “n” is higher or lower than expected

# Print table
knitr::kable(tbl, align="l", caption="Number of cells per cluster per sample") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%")

Number of cells per cluster per sample

	Cl_1	Cl_2	Cl_3
pbmc_10x_n	46	33	10
pbmc_10x_perc	51.11	45.21	38.46
pbmc_10x.oddsRatio	1.36	0.88	0.67
pbmc_10x.p	1.8e-01	7.1e-01	8.8e-01
pbmc_smartseq2_sample1_n	44	40	16
pbmc_smartseq2_sample1_perc	48.89	54.79	61.54
pbmc_smartseq2_sample1.oddsRatio	0.74	1.13	1.5
pbmc_smartseq2_sample1.p	8.9e-01	4.0e-01	2.3e-01

# Reset default assay, so we won't plot integrated data
# Note: We need integrated data for UMAP, clusters
DefaultAssay(sc) = param$norm

Cell Cycle Effect

How much do gene expression profiles in the dataset reflect the cell cycle phases the single cells were in? After initial normalisation, we determined the effects of cell cycle heterogeneity by calculating a score for each cell based on its expression of G2M and S phase markers. Scoring is based on the strategy described in Tirosh et al. (2016), and human gene symbols are translated to gene symbols of the species of interest using biomaRt. This section of the report visualises the above calculated cell cycle scores.

# Set up colours for cell cycle effect and add to sc object
col =  GenerateColours(num_colours=length(levels(sc$Phase)), names=levels(sc$Phase), palette="ggsci::pal_npg", alphas=1)
sc = ScAddLists(sc, lists=list(Phase=col), lists_slot="colour_lists")

# Get a feeling for how many cells are affected
p1 = ggplot(sc[[]], aes(x=S.Score, y=G2M.Score, colour=Phase)) + 
  geom_point() + 
  scale_x_continuous("G1/S score") + 
  scale_y_continuous("G2/M score") + 
  AddStyle(col=Misc(sc, "colour_lists")[["Phase"]])

p2 = ggplot(sc@meta.data %>% 
              dplyr::group_by(seurat_clusters, Phase) %>% 
              dplyr::summarise(num_cells=length(Phase)), 
            aes(x=seurat_clusters, y=num_cells, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_x_discrete("Seurat clusters") + 
  scale_y_continuous("Fraction of cells") + 
  AddStyle(fill=Misc(sc, "colour_lists")[["Phase"]])

p3 = ggplot(sc[[]] %>% 
              dplyr::group_by(orig.ident, Phase) %>% 
              dplyr::summarise(num_cells=length(Phase)), 
            aes(x=orig.ident, y=num_cells, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_y_continuous("Fraction of cells") +
  AddStyle(fill=Misc(sc, "colour_lists")[["Phase"]]) + 
  theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1)) + xlab("")

p = p1 + p2 + p3 & theme(legend.position="bottom")
p = p + patchwork::plot_annotation(title="Cell cycle phases") + plot_layout(guides="collect")
p

UMAP coloured by cell cycle phases

if (any(!is.na(sc$Phase))) {
  # UMAP with phases superimposed
  # Note: This is a hack to colour by phase but label by Cluster
  p = Seurat::DimPlot(sc, group.by="Phase", pt.size=1, cols=Misc(sc, "colour_lists")[["Phase"]]) + 
    AddStyle(title="UMAP, cells coloured by cell cycle phases", legend_title="Phase")
  p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
  p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
  p
}

UMAP coloured by S phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="S.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) +
    AddStyle(title="UMAP, cells coloured by S phase")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

UMAP coloured by G2/M phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="G2M.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) + 
    AddStyle(title="UMAP, cells coloured by G2M phase")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

UMAP coloured by the difference between S and G2/M phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="CC.Difference", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) +
    AddStyle(title="UMAP, cells coloured by CC.Difference")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

Cluster QC

Do cells in individual clusters have particularly high counts, detected genes or mitochondrial content?

Number of counts

p1 = suppressMessages(Seurat::FeaturePlot(sc, features="nCount_RNA") + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))
p1 = LabelClusters(p1, id="ident", box=FALSE)


p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=nCount_RNA, fill=seurat_clusters, group=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot (log10 scale)", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title="Summed raw counts (nCount_RNA, log10 scale)")
p

Number of features

p1 = suppressMessages(Seurat::FeaturePlot(sc, features="nFeature_RNA") + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=nFeature_RNA, fill=seurat_clusters, group=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title="Number of features with raw count > 0 (nFeature_RNA, log10 scale)")
p

Percent mitochondrial reads

p1 = Seurat::FeaturePlot(sc, features="percent_mt", cols=c("lightgrey", param$col)) + 
  AddStyle(title="Feature plot")
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_mt, fill=seurat_clusters, group=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none")
p = p1 | p2 
p = p + patchwork::plot_annotation(title="Percent of mitochondrial features (percent_mt)")
p

Percent ribosomal reads

p1 = Seurat::FeaturePlot(sc, features="percent_ribo", cols=c("lightgrey", param$col)) +
  AddStyle(title="Feature plot")
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_ribo, fill=seurat_clusters, group=seurat_clusters)) +
  geom_violin(scale="width") +
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none")
p = p1 | p2
p = p + patchwork::plot_annotation(title="Percent of ribosomal features (percent_ribo)")
p

Known marker genes

Do cells in individual clusters express provided known marker genes?

known_markers_list=c()

# Overwrite empty list of known markers 
if (!is.null(param$file_known_markers)) {
  # Read known marker genes and map to rownames
  known_markers = openxlsx::read.xlsx(param$file_known_markers)
  known_markers_list = lapply(colnames(known_markers), function(x) {
    y = ensembl_to_seurat_rowname[known_markers[,x]] %>% 
      na.exclude() %>% unique() %>% sort()
    m = !y %in% rownames(sc)
    if (any(m)){
      Warning(paste0("The following genes of marker list '", x, "' cannot be found in the data: ", first_n_elements_to_string(y[m], n=10)))
    }
    return(y[!m])
  })
  
  # Remove empty lists
  names(known_markers_list) = colnames(known_markers)
  is_empty = purrr::map_int(known_markers_list, .f=length) == 0 
  known_markers_list = known_markers_list[!is_empty]
  
  # Add lists to sc object
  sc = ScAddLists(sc, lists=setNames(known_markers_list, paste0("known_marker_", names(known_markers_list))), lists_slot="gene_lists")
}  

# Set plot options
if(length(known_markers_list) > 0) { 
  known_markers_n = length(known_markers_list) 
  known_markers_vect = unlist(known_markers_list) %>% unique() %>% sort()
  idx_dotplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) <= 50)
  idx_avgplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) >= 10)
} else { 
  known_markers_n=0
  idx_dotplot = idx_avgplot = FALSE
  known_markers_vect = c()
}

# Dotplots and average feature plots
# The height of 1 row (= 1 plot) is fixed to 5 
fig_height_knownMarkers_dotplot = max(5, 5 * sum(idx_dotplot))
fig_height_knownMarkers_avgplot = max(5, 5 * sum(idx_avgplot))

# Individual feature plots
# Each row contains 2 plots
# We fix the height of each plot to the same height as is used later for DEGs
height_per_row = max(2, 0.3 * length(levels(sc$seurat_clusters)))
nr_rows = ceiling(length(known_markers_vect)/2)
fig_height_knownMarkers_vect = max(5, height_per_row * nr_rows)

You provided 6 list(s) of known marker genes. In the following tabs, you find:

• Dot plots for all gene lists containing at most 50 genes
  
• Average feature plots for all gene lists containing at least 10 genes
  
• Individual feature plots for all genes if there are no more than 100 genes in total

Dot plot(s)

A dot plot visualises how gene expression changes across different clusters. The size of a dot encodes the percentage of cells in a cluster that expresses the gene, while the color encodes the scaled average expression across all cells within the cluster. Per gene, we group cells based on cluster identity, calculate average expression per cluster, subtract the mean of average expression values and divide by the standard deviation. The resulting scores describe how high or low a gene is expressed in a cluster compared to all other clusters.

if ((known_markers_n > 0) & any(idx_dotplot)) {
  known_markers_dotplot = known_markers_list[idx_dotplot]
  p_list = list()
  for (i in seq(known_markers_dotplot)) {
    g = known_markers_dotplot[[i]]
    g = g[length(g):1]
    p_list[[i]] = suppressMessages(
      Seurat::DotPlot(sc, features=g) + 
        scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
        AddStyle(title=paste("Known marker genes:", names(known_markers_dotplot)[i]), ylab="Cluster") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) +
        lims(size=c(0,100))
      )
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  p
} else if ((known_markers_n > 0) & !any(idx_dotplot)) {
  message("This tab is used for dot plots for up to 50 genes. All provided lists are longer than this, and hence dot plots are skipped.")
} else {
  message("No known marker genes were provided and hence dot plots are skipped.")
}

Average feature plot(s)

An average feature plot visualises the average gene expression of each gene list on a single-cell level, subtracted by the aggregated expression of control feature sets. The color of the plot encodes the calculated scores, whereat positive scores suggest that genes are expressed more highly than expected.

if ((known_markers_n > 0) & any(idx_avgplot)) {
  known_markers_avgplot = known_markers_list[idx_avgplot]
  sc = Seurat::AddModuleScore(sc, features=known_markers_avgplot, ctrl=10, name="known_markers")
  idx_replace_names = grep("^known_markers[0-9]+$", colnames(sc@meta.data), perl=TRUE)
  colnames(sc@meta.data)[idx_replace_names] = names(known_markers_avgplot)
  p_list = Seurat::FeaturePlot(sc, features=names(known_markers_avgplot), cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(known_markers_avgplot)) {
    p_list[[i]] = p_list[[i]] + AddStyle(title=paste("Known marker genes:", names(known_markers_avgplot)[i]))
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  print(p)
} else if ((known_markers_n > 0) & !any(idx_avgplot)) {
  message("This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence average feature plots are skipped.")
}

× (Message)
This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.

Individual feature plots

An individual feature plot colours single cells on the UMAP according to their normalised gene expression.

if ((known_markers_n > 0) & length(known_markers_vect) <= 100) {
  p_list = Seurat::FeaturePlot(sc, features=known_markers_vect, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
  p = patchwork::wrap_plots(p_list, ncol=2)
  print(p)
} else if (length(known_markers_vect) > 100) { 
  message("This tab is used to plot up to 100 known marker genes. Your provided list is longer than this, and hence individual feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence individual feature plots are skipped.")
}

Marker genes

We next identify genes that are differentially expressed in one cluster compared to all other clusters, based on raw “RNA” data and the method “MAST.” Resulting p-values are adjusted using the Bonferroni method. However, note that the p-values are likely inflated, since both clusters and marker genes were determined based on the same gene expression data, and there ought to be gene expression differences by design. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

# Find DEGs for every cluster compared to all remaining cells, report positive (=markers) and negative ones
# min.pct = requires feature to be detected at this minimum percentage in either of the two groups of cells 
# logfc.threshold = requires a feature to be differentially expressed on average by some amount between the two groups
# only.pos = find only positive markers 

# Review recommends using "MAST"; Mathias uses "LR"
# ALWAYS USE: assay="RNA" or assay="SCT"
# DONT USE: assay=integrated datasets; this data is normalised and contains only 2k genes
# Note: By default, the function uses slot="data". Mast requires log data, so this is the correct way to do it.
#   https://www.bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAST-interoperability.html
markers = suppressMessages(Seurat::FindAllMarkers(sc, assay="RNA", test.use="MAST",
                                               only.pos=FALSE, min.pct=param$marker_pct, logfc.threshold=param$marker_log2FC,
                                               latent.vars=param$latent_vars, verbose=FALSE, silent=TRUE))

# If no markers were found, initialise the degs table so that further downstream (export) chunks run
if (ncol(markers)==0) markers = DegsEmptyMarkerResultsTable(levels(sc$seurat_clusters))

# For Seurat versions until 3.2, log fold change is based on the natural log. Convert to log base 2.
if ("avg_logFC" %in% colnames(markers) & !"avg_log2FC" %in% colnames(markers)) {
  lfc_idx = grep("avg_log\\S*FC", colnames(markers))
  markers[,lfc_idx] = marker_deg_results[,lfc_idx] / log(2)
  col_nms = colnames(markers)
  col_nms[2] = "avg_log2FC"
  colnames(markers) = col_nms
}

# Sort markers
markers = markers %>% DegsSort(group=c("cluster"))
  
# Filter markers 
markers_filt = DegsFilter(markers, cut_log2FC=param$marker_log2FC, cut_padj=param$marker_padj)
markers_found = nrow(markers_filt$all)>0

# Add average data to table
markers_out = cbind(markers_filt$all, DegsAvgDataPerIdentity(sc, genes=markers_filt$all$gene))

# Split by cluster and write to file
additional_readme = data.frame(Column=c("cluster",
                                        "p_val_adj_score",
                                        "avg_<assay>_<slot>_id<cluster>"), 
                               Description=c("Cluster",
                                             "Score calculated as follows: -log10(p_val_adj)*sign(avg_log2FC)",
                                             "Average expression value for cluster; <assay>: RNA or SCT; <slot>: raw counts or normalised data"))

invisible(DegsWriteToFile(split(markers_out, markers_out$cluster),
                                       annot_ensembl=annot_ensembl,
                                       gene_to_ensembl=seurat_rowname_to_ensembl,
                                       additional_readme=additional_readme,
                                       file=file.path(param$path_out, "marker_degs", "markers_cluster_vs_rest.xlsx")))


# Plot number of differentially expressed genes
p = DegsPlotNumbers(markers_filt$all, 
                      group="cluster", 
                      title=paste0("Number of DEGs, comparing each cluster to the rest\n(FC=", 2^param$marker_log2FC, ", adj. p-value=", param$marker_padj, ")")) 

# Add marker table to seurat object
Seurat::Misc(sc, "markers") = list(condition_column="seurat_clusters", test="MAST", padj=param$marker_padj, 
                                   log2FC=param$marker_log2FC, min_pct=param$marker_pct, assay="RNA", slot="data",
                                   latent_vars=param$latent_vars,
                                   results=markers_filt$all)

# Add marker lists to seurat object
marker_genesets_up = split(markers_filt$up$gene, markers_filt$up$cluster)
names(marker_genesets_up) = paste0("markers_up_cluster", names(marker_genesets_up))
marker_genesets_down = split(markers_filt$down$gene, markers_filt$down$cluster)
names(marker_genesets_down) = paste0("markers_down_cluster", names(marker_genesets_down))
sc = ScAddLists(sc, lists=c(marker_genesets_up, marker_genesets_down), lists_slot="gene_lists")

if (markers_found) {
  p
} else {
  warning("No differentially expressed genes (cluster vs rest) found. The following related code is not executed, no related plots and tables are generated.")
}

Table of top marker genes

We use the term “marker genes” to specifically describe genes that are up-regulated in cells of one cluster compared to the rest.

if (markers_found) {
  markers_top = DegsUpDisplayTop(markers_filt$up, n=5)
  
  # Add labels
  markers_top$labels = paste0(markers_top$cluster, ": ", markers_top$gene)

  # Show table
  knitr::kable(markers_top %>% dplyr::select(-labels), align="l", caption="Up to top 5 marker genes per cell cluster") %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%", height="700px") 
}

Up to top 5 marker genes per cell cluster

cluster	gene	avg_log2FC	p_val	p_val_adj	pct.1	pct.2
1	IL7R	2.859	8.7e-25	1.2e-20	0.711	0.071
1	RPL13	1.046	1.4e-22	2.1e-18	0.944	0.990
1	LTB	2.171	6.6e-18	9.5e-14	0.689	0.101
1	RPSA	1.057	1.1e-14	1.6e-10	0.944	0.970
1	CCR7	1.643	1.5e-11	2.1e-07	0.311	0.000
2	NKG7	2.746	1.0e-31	1.5e-27	0.973	0.379
2	GZMH	2.581	1.6e-28	2.3e-24	0.877	0.095
2	FGFBP2	3.262	1.2e-25	1.8e-21	0.808	0.086
2	PRF1	2.536	2.6e-22	3.8e-18	0.904	0.250
2	CST7	1.973	2.7e-21	3.9e-17	0.932	0.319
3	LYZ	6.215	2.1e-32	3.0e-28	1.000	0.104
3	SERPINA1.1	3.898	4.2e-30	6.0e-26	0.962	0.012
3	CLEC7A	2.599	4.0e-29	5.7e-25	1.000	0.043
3	FTL	2.475	1.8e-28	2.6e-24	1.000	0.994
3	CTSS	3.994	2.4e-28	3.4e-24	1.000	0.362

Visualisation of top marker genes

The following plots visualise the top marker genes for each cluster, respectively. Clear marker genes indicate good clusters that represent cell types.

# Note: We need to run this chunk as it specifies a variable that is used in chunk definitions below
if (markers_found) {

  # Feature plots and violin plots: each row contains 5 plots
  #   Row height is not dependent on the number of clusters
  #   The plot has 5 columns and 1 row per cluster, hence the layout works nicely if we find 
  #     at least 5 markers per cluster
  nr_rows_5cols = ceiling(nrow(markers_top)/5)
  fig_height_5cols = nr_rows_5cols * 3
  
  # Dotplots: each row contains 2 plots
  #   Row height is dependent on the number of clusters 
  nr_rows_dp_2cols = ceiling(length(levels(sc$seurat_clusters))/2)
  fig_height_dp_2cols = nr_rows_dp_2cols * max(4, 0.5 * length(levels(sc$seurat_clusters)))
  
} else {
  fig_height_5cols = fig_height_dp_2cols = 7 
}

Feature plots

if (markers_found) {
  # Plot each marker one by one, and then combine them all at the end
  p_list = list()
  for (i in 1:nrow(markers_top)) { 
    p_list[[i]] = Seurat::FeaturePlot(sc, features=markers_top$gene[i], 
                                      cols=c("lightgrey", param$col_clusters[markers_top$cluster[i]]),  
                                      combine=TRUE, label=TRUE) + 
      AddStyle(title=markers_top$labels[i], 
               xlab="", ylab="", 
               legend_position="bottom")
  }
  
  # Combine all plots
  p = patchwork::wrap_plots(p_list, ncol=5) + 
    patchwork::plot_annotation(title="UMAP, cells coloured by normalised gene expression data, top marker genes per cluster")
  p
}

Violin plots (normalised)

if (markers_found) {
  # Plot violin plots per marker gene, and combine it all at the end
  # This layout works out nicely if there are 5 marker genes per cluster
  p_list = list()
  for(i in 1:nrow(markers_top)) { 
    p_list[[i]] = Seurat::VlnPlot(sc, features=markers_top$gene[i], assay="RNA", pt.size=0, cols=param$col_clusters) + 
      AddStyle(title=markers_top$labels[i], xlab="")
  }
  p = patchwork::wrap_plots(p_list, ncol=5) + 
    patchwork::plot_annotation(title="Violin plot of for normalised gene expression data, top marker genes per cluster") & theme(legend.position="none")
  p
}

Dot plot (scaled)

if (markers_found) {
  # Visualises how feature expression changes across different clusters
  # Plot dotplots per cluster, and combine it all at the end
  p_list = lapply(markers_top$cluster %>% sort() %>% unique(), function(cl) {
    genes = markers_top %>% dplyr::filter(cluster==cl) %>% dplyr::pull(gene)
    p = suppressMessages(Seurat::DotPlot(sc, features=genes) + 
                           scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
                           AddStyle(title=paste0("Top marker genes for cluster ", cl, " (scaled)"), ylab="Cluster", legend_position="bottom") + 
                           theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
                           guides(size=guide_legend(order=1)))
    return(p)
  })
  
  p = patchwork::wrap_plots(p_list, ncol=2) 
  p
}

Dot plot (non-scaled)

if (markers_found) {
  # Visualises how feature expression changes across different clusters
  # Plot dotplots per cluster, and combine it all at the end
  p_list = lapply(markers_top$cluster %>% sort() %>% unique(), function(cl) {
    genes = markers_top %>% dplyr::filter(cluster==cl) %>% dplyr::pull(gene)
    genes = genes[length(genes):1]
    p = suppressMessages(DotPlotUpdated(sc, features=genes, scale=FALSE, cols=c("lightgrey", param$col)) + 
                           AddStyle(title=paste0("Top marker genes for cluster ", cl, " (not scaled)"), ylab="Cluster", legend_position="bottom") + 
                           theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
                           guides(size=guide_legend(order=1)))
    return(p)
  })
      
  p = patchwork::wrap_plots(p_list, ncol=2)
  p
}

Expression per cluster per sample

If the dataset contains multiple samples, we can visualise the expression of a gene that is up-regulated in a cluster separately for each sample. For each cluster, we extract up-regulated genes, and visualise expression of these genes in all cells in that cluster, split by their sample of origin.

fig_height_degs_per_cl = max(5, 
                             max(2, 0.3 * (sc$orig.ident %>% unique() %>% length())) * length(levels(sc$seurat_clusters)) * 2)

Scaled dotplots

First, we plot scaled expression as explained above (see section Known marker genes). This plot allows us to judge whether the expression of a gene is increased in one sample as compared to the other samples.

if (markers_found) {
  p_list = list()
  markers_filt_up_top = DegsUpDisplayTop(degs=markers_filt$up, n=50)
  for (cl in levels(sc$seurat_clusters)) {  
    markers_filt_up_cl_top = markers_filt_up_top %>% 
      dplyr::filter(cluster==cl) %>% 
      dplyr::pull(gene)

    if (length(markers_filt_up_cl_top) > 0) {
      p_list[[cl]] = suppressMessages(Seurat::DotPlot(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident") +
        scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") + 
        AddStyle(title=paste0("Up to 50 markers (up-regulated genes) for cluster ", cl), ylab="Cluster", legend_position="bottom") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) + 
        guides(size=guide_legend(order=1)))
    }
  }
  p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster") 
  p
}

Non-scaled dotplots

Second, we plot normalised expression with no further scaling. This plot helps to get an impression of the total expression of a gene.

if (markers_found) {
  n_genes_max_dotplot = 50
  p_list = list()
  for (cl in levels(sc$seurat_clusters)) {            
    markers_filt_up_cl_top = markers_filt$up %>% 
      dplyr::filter(cluster==cl) %>% 
      dplyr::top_n(n=n_genes_max_dotplot, wt=p_val_adj_score) %>% 
      dplyr::pull(gene)
    if (length(markers_filt_up_cl_top) > 0) {
      p_list[[cl]] = DotPlotUpdated(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident", scale=FALSE, cols=c("lightgrey", param$col)) +
        AddStyle(title=paste0("Up to ", n_genes_max_dotplot, " markers (up-regulated genes) for cluster ", cl, " (not scaled)"), ylab="Cluster", legend_position="bottom") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) + 
        guides(size=guide_legend(order=1))
    }
  }
  
  p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster (not scaled)") 
  p
}

Heatmaps

All up- and down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
    
  # Heatmap of all differentially expressed genes
  p = Seurat::DoHeatmap(sc, features=markers_filt$all$gene, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_blank()) +
    ggtitle("Heatmap of scaled gene expression data, all genes differentially expressed between a cluster and the rest")
  p
}

Top 300 up-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$up$cluster))
  features_subset = markers_filt$up %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=avg_log2FC) %>% 
    dplyr::arrange(cluster, -avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top up-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Top 300 down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$down$cluster))
  features_subset = markers_filt$down %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=-avg_log2FC) %>% 
    dplyr::arrange(cluster, avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top down-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Functional enrichment analysis

To gain first insights into potential functions of cells in a cluster, we test for over-representation of functional terms amongst up- and down-regulated genes of each cluster. Over-represented terms are written to file.

We first translate gene symbols of up- and down-regulated genes per cluster into Entrez gene symbols, and then use the “enrichR” R-package to access the “Enrichr” website https://amp.pharm.mssm.edu/Enrichr/. You can choose to test functional enrichment from a wide range of databases:

dbs_all = enrichR::listEnrichrDbs()
knitr::kable(dbs_all, align="l", caption="Enrichr databases") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%", height="300px")

Enrichr databases

geneCoverage	genesPerTerm	libraryName	link	numTerms	appyter
13362	275	Genome_Browser_PWMs	http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/	615	ea115789fcbf12797fd692cec6df0ab4dbc79c6a
27884	1284	TRANSFAC_and_JASPAR_PWMs	http://jaspar.genereg.net/html/DOWNLOAD/	326	7d42eb43a64a4e3b20d721fc7148f685b53b6b30
6002	77	Transcription_Factor_PPIs		290	849f222220618e2599d925b6b51868cf1dab3763
47172	1370	ChEA_2013	http://amp.pharm.mssm.edu/lib/cheadownload.jsp	353	7ebe772afb55b63b41b79dd8d06ea0fdd9fa2630
47107	509	Drug_Perturbations_from_GEO_2014	http://www.ncbi.nlm.nih.gov/geo/	701	ad270a6876534b7cb063e004289dcd4d3164f342
21493	3713	ENCODE_TF_ChIP-seq_2014	http://genome.ucsc.edu/ENCODE/downloads.html	498	497787ebc418d308045efb63b8586f10c526af51
1295	18	BioCarta_2013	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	249	4a293326037a5229aedb1ad7b2867283573d8bcd
3185	73	Reactome_2013	http://www.reactome.org/download/index.html	78	b343994a1b68483b0122b08650201c9b313d5c66
2854	34	WikiPathways_2013	http://www.wikipathways.org/index.php/Download_Pathways	199	5c307674c8b97e098f8399c92f451c0ff21cbf68
15057	300	Disease_Signatures_from_GEO_up_2014	http://www.ncbi.nlm.nih.gov/geo/	142	248c4ed8ea28352795190214713c86a39fd7afab
4128	48	KEGG_2013	http://www.kegg.jp/kegg/download/	200	eb26f55d3904cb0ea471998b6a932a9bf65d8e50
34061	641	TF-LOF_Expression_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	269
7504	155	TargetScan_microRNA	http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61	222	f4029bf6a62c91ab29401348e51df23b8c44c90f
16399	247	PPI_Hub_Proteins	http://amp.pharm.mssm.edu/X2K	385	69c0cfe07d86f230a7ef01b365abcc7f6e52f138
12753	57	GO_Molecular_Function_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	1136	f531ac2b6acdf7587a54b79b465a5f4aab8f00f9
23726	127	GeneSigDB	https://pubmed.ncbi.nlm.nih.gov/22110038/	2139	6d655e0aa3408a7accb3311fbda9b108681a8486
32740	85	Chromosome_Location	http://software.broadinstitute.org/gsea/msigdb/index.jsp	386	8dab0f96078977223646ff63eb6187e0813f1433
13373	258	Human_Gene_Atlas	http://biogps.org/downloads/	84	0741451470203d7c40a06274442f25f74b345c9c
19270	388	Mouse_Gene_Atlas	http://biogps.org/downloads/	96	31191bfadded5f96983f93b2a113cf2110ff5ddb
13236	82	GO_Cellular_Component_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	641	e1d004d5797cbd2363ef54b1c3b361adb68795c6
14264	58	GO_Biological_Process_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	5192	bf120b6e11242b1a64c80910d8e89f87e618e235
3096	31	Human_Phenotype_Ontology	http://www.human-phenotype-ontology.org/	1779	17a138b0b70aa0e143fe63c14f82afb70bc3ed0a
22288	4368	Epigenomics_Roadmap_HM_ChIP-seq	http://www.roadmapepigenomics.org/	383	e1bc8a398e9b21f9675fb11bef18087eda21b1bf
4533	37	KEA_2013	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	474	462045609440fa1e628a75716b81a1baa5bd9145
10231	158	NURSA_Human_Endogenous_Complexome	https://www.nursa.org/nursa/index.jsf	1796	7d3566b12ebc23dd23d9ca9bb97650f826377b16
2741	5	CORUM	http://mips.helmholtz-muenchen.de/genre/proj/corum/	1658	d047f6ead7831b00566d5da7a3b027ed9196e104
5655	342	SILAC_Phosphoproteomics	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	84	54dcd9438b33301deb219866e162b0f9da7e63a0
10406	715	MGI_Mammalian_Phenotype_Level_3	http://www.informatics.jax.org/	71	c3bfc90796cfca8f60cba830642a728e23a53565
10493	200	MGI_Mammalian_Phenotype_Level_4	http://www.informatics.jax.org/	476	0b09a9a1aa0af4fc7ea22d34a9ae644d45864bd6
11251	100	Old_CMAP_up	http://www.broadinstitute.org/cmap/	6100	9041f90cccbc18479138330228b336265e09021c
8695	100	Old_CMAP_down	http://www.broadinstitute.org/cmap/	6100	ebc0d905b3b3142f936d400c5f2a4ff926c81c37
1759	25	OMIM_Disease	http://www.omim.org/downloads	90	cb2b92578a91e023d0498a334923ee84add34eca
2178	89	OMIM_Expanded	http://www.omim.org/downloads	187	27eca242904d8e12a38cf8881395bc50d57a03e1
851	15	VirusMINT	http://mint.bio.uniroma2.it/download.html	85	5abad1fc36216222b0420cadcd9be805a0dda63e
10061	106	MSigDB_Computational	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	858	e4cdcc7e259788fdf9b25586cce3403255637064
11250	166	MSigDB_Oncogenic_Signatures	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	189	c76f5319c33c4833c71db86a30d7e33cd63ff8cf
15406	300	Disease_Signatures_from_GEO_down_2014	http://www.ncbi.nlm.nih.gov/geo/	142	aabdf7017ae55ae75a004270924bcd336653b986
17711	300	Virus_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	323	45268b7fc680d05dd9a29743c2f2b2840a7620bf
17576	300	Virus_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	323	5f531580ccd168ee4acc18b02c6bdf8200e19d08
15797	176	Cancer_Cell_Line_Encyclopedia	https://portals.broadinstitute.org/ccle/home	967	eb38dbc3fb20adafa9d6f9f0b0e36f378e75284f
12232	343	NCI-60_Cancer_Cell_Lines	http://biogps.org/downloads/	93	75c81676d8d6d99d262c9660edc024b78cfb07c9
13572	301	Tissue_Protein_Expression_from_ProteomicsDB	https://www.proteomicsdb.org/	207
6454	301	Tissue_Protein_Expression_from_Human_Proteome_Map	http://www.humanproteomemap.org/index.php	30	49351dc989f9e6ca97c55f8aca7778aa3bfb84b9
3723	47	HMDB_Metabolites	http://www.hmdb.ca/downloads	3906	1905132115d22e4119bce543bdacaab074edb363
7588	35	Pfam_InterPro_Domains	ftp://ftp.ebi.ac.uk/pub/databases/interpro/	311	e2b4912cfb799b70d87977808c54501544e4cdc9
7682	78	GO_Biological_Process_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	941	5216d1ade194ffa5a6c00f105e2b1899f64f45fe
7324	172	GO_Cellular_Component_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	205	fd1332a42395e0bc1dba82868b39be7983a48cc5
8469	122	GO_Molecular_Function_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	402	7e3e99e5aae02437f80b0697b197113ce3209ab0
13121	305	Allen_Brain_Atlas_up	http://www.brain-map.org/	2192	3804715a63a308570e47aa1a7877f01147ca6202
26382	1811	ENCODE_TF_ChIP-seq_2015	http://genome.ucsc.edu/ENCODE/downloads.html	816	56b6adb4dc8a2f540357ef992d6cd93dfa2907e5
29065	2123	ENCODE_Histone_Modifications_2015	http://genome.ucsc.edu/ENCODE/downloads.html	412	55b56cd8cf2ff04b26a09b9f92904008b82f3a6f
280	9	Phosphatase_Substrates_from_DEPOD	http://www.koehn.embl.de/depod/	59	d40701e21092b999f4720d1d2b644dd0257b6259
13877	304	Allen_Brain_Atlas_down	http://www.brain-map.org/	2192	ea67371adec290599ddf484ced2658cfae259304
15852	912	ENCODE_Histone_Modifications_2013	http://genome.ucsc.edu/ENCODE/downloads.html	109	c209ae527bc8e98e4ccd27a668d36cd2c80b35b4
4320	129	Achilles_fitness_increase	http://www.broadinstitute.org/achilles	216	98366496a75f163164106e72439fb2bf2f77de4e
4271	128	Achilles_fitness_decrease	http://www.broadinstitute.org/achilles	216	83a710c1ff67fd6b8af0d80fa6148c40dbd9bc64
10496	201	MGI_Mammalian_Phenotype_2013	http://www.informatics.jax.org/	476	a4c6e217a81a4a58ff5a1c9fc102b70beab298e9
1678	21	BioCarta_2015	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	239	70e4eb538daa7688691acfe5d9c3c19022be832b
756	12	HumanCyc_2015	http://humancyc.org/	125	711f0c02b23f5e02a01207174943cfeee9d3ea9c
3800	48	KEGG_2015	http://www.kegg.jp/kegg/download/	179	e80d25c56de53c704791ddfdc6ab5eec28ae7243
2541	39	NCI-Nature_2015	http://pid.nci.nih.gov/	209	47edfc012bcbb368a10b717d8dca103f7814b5a4
1918	39	Panther_2015	http://www.pantherdb.org/	104	ab824aeeff0712bab61f372e43aebb870d1677a9
5863	51	WikiPathways_2015	http://www.wikipathways.org/index.php/Download_Pathways	404	1f7eea2f339f37856522c1f1c70ec74c7b25325f
6768	47	Reactome_2015	http://www.reactome.org/download/index.html	1389	36e541bee015eddb8d53827579549e30fe7a3286
25651	807	ESCAPE	http://www.maayanlab.net/ESCAPE/	315	a7acc741440264717ff77751a7e5fed723307835
19129	1594	HomoloGene	http://www.ncbi.nlm.nih.gov/homologene	12	663b665b75a804ef98add689f838b68e612f0d2a
23939	293	Disease_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	839	0f412e0802d76efa0374504c2c9f5e0624ff7f09
23561	307	Disease_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	839	9ddc3902fb01fb9eaf1a2a7c2ff3acacbb48d37e
23877	302	Drug_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	906	068623a05ecef3e4a5e0b4f8db64bb8faa3c897f
15886	9	Genes_Associated_with_NIH_Grants	https://grants.nih.gov/grants/oer.htm	32876	76fc5ec6735130e287e62bae6770a3c5ee068645
24350	299	Drug_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	906	c9c2155b5ac81ac496854fa61ba566dcae06cc80
3102	25	KEA_2015	http://amp.pharm.mssm.edu/Enrichr	428	18a081774e6e0aaf60b1a4be7fd20afcf9e08399
31132	298	Gene_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	2460	53dedc29ce3100930d68e506f941ef59de05dc6b
30832	302	Gene_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	2460	499882af09c62dd6da545c15cb51c1dc5e234f78
48230	1429	ChEA_2015	http://amp.pharm.mssm.edu/Enrichr	395	712eb7b6edab04658df153605ec6079fa89fb5c7
5613	36	dbGaP	http://www.ncbi.nlm.nih.gov/gap	345	010f1267055b1a1cb036e560ea525911c007a666
9559	73	LINCS_L1000_Chem_Pert_up	https://clue.io/	33132	5e678b3debe8d8ea95187d0cd35c914017af5eb3
9448	63	LINCS_L1000_Chem_Pert_down	https://clue.io/	33132	fedbf5e221f45ee60ebd944f92569b5eda7f2330
16725	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_down	http://www.gtexportal.org/	2918	74b818bd299a9c42c1750ffe43616aa9f7929f02
19249	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_up	http://www.gtexportal.org/	2918	103738763d89cae894bec9f145ac28167a90e611
15090	282	Ligand_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	261	1eb3c0426140340527155fd0ef67029db2a72191
16129	292	Aging_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	286	cd95fe1b505ba6f28cd722cfba50fdea979d3b4c
15309	308	Aging_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	286	74c4f0a0447777005b2a5c00c9882a56dfc62d7c
15103	318	Ligand_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	261	31baa39da2931ddd5f7aedf2d0bbba77d2ba7b46
15022	290	MCF7_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	401	555f68aef0a29a67b614a0d7e20b6303df9069c6
15676	310	MCF7_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	401	1bc2ba607f1ff0dda44e2a15f32a2c04767da18c
15854	279	Microbe_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	312	9e613dba78ef7e60676b13493a9dc49ccd3c8b3f
15015	321	Microbe_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	312	d0c3e2a68e8c611c669098df2c87b530cec3e132
3788	159	LINCS_L1000_Ligand_Perturbations_down	https://clue.io/	96	957846cb05ef31fc8514120516b73cc65af7980e
3357	153	LINCS_L1000_Ligand_Perturbations_up	https://clue.io/	96	3bd494146c98d8189898a947f5ef5710f1b7c4b2
12668	300	L1000_Kinase_and_GPCR_Perturbations_down	https://clue.io/	3644	1ccc5bce553e0c2279f8e3f4ddcfbabcf566623b
12638	300	L1000_Kinase_and_GPCR_Perturbations_up	https://clue.io/	3644	b54a0d4ba525eac4055c7314ca9d9312adcb220c
8973	64	Reactome_2016	http://www.reactome.org/download/index.html	1530	1f54638e8f45075fb79489f0e0ef906594cb0678
7010	87	KEGG_2016	http://www.kegg.jp/kegg/download/	293	43f56da7540195ba3c94eb6e34c522a699b36da9
5966	51	WikiPathways_2016	http://www.wikipathways.org/index.php/Download_Pathways	437	340be98b444cad50bb974df69018fd598e23e5e1
15562	887	ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X		104	5426f7747965c23ef32cff46fabf906e2cd76bfa
17850	300	Kinase_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	285	bb9682d78b8fc43be842455e076166fcd02cefc3
17660	300	Kinase_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	285	78618915009cac3a0663d6f99d359e39a31b6660
1348	19	BioCarta_2016	http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	237	13d9ab18921d5314a5b2b366f6142b78ab0ff6aa
934	13	HumanCyc_2016	http://humancyc.org/	152	d6a502ef9b4c789ed5e73ca5a8de372796e5c72a
2541	39	NCI-Nature_2016	http://pid.nci.nih.gov/	209	3c1e1f7d1a651d9aaa198e73704030716fc09431
2041	42	Panther_2016	http://www.pantherdb.org/pathway/	112	ca5f6abf7f75d9baae03396e84d07300bf1fd051
5209	300	DrugMatrix	https://ntp.niehs.nih.gov/drugmatrix/	7876	255c3db820d612f34310f22a6985dad50e9fe1fe
49238	1550	ChEA_2016	http://amp.pharm.mssm.edu/Enrichr	645	af271913344aa08e6a755af1d433ef15768d749a
2243	19	huMAP	http://proteincomplexes.org/	995	249247d2f686d3eb4b9e4eb976c51159fac80a89
19586	545	Jensen_TISSUES	http://tissues.jensenlab.org/	1842	e8879ab9534794721614d78fe2883e9e564d7759
22440	505	RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	1302	f0752e4d7f5198f86446678966b260c530d19d78
8184	24	MGI_Mammalian_Phenotype_2017	http://www.informatics.jax.org/	5231	0705e59bff98deda6e9cbe00cfcdd871c85e7d04
18329	161	Jensen_COMPARTMENTS	http://compartments.jensenlab.org/	2283	56ec68c32d4e83edc2ee83bea0e9f6a3829b2279
15755	28	Jensen_DISEASES	http://diseases.jensenlab.org/	1811	3045dff8181367c1421627bb8e4c5a32c6d67f98
10271	22	BioPlex_2017	http://bioplex.hms.harvard.edu/	3915	b8620b1a9d0d271d1a2747d8cfc63589dba39991
10427	38	GO_Cellular_Component_2017	http://www.geneontology.org/	636	8fed21d22dfcc3015c05b31d942fdfc851cc8e04
10601	25	GO_Molecular_Function_2017	http://www.geneontology.org/	972	b4018906e0a8b4e81a1b1afc51e0a2e7655403eb
13822	21	GO_Biological_Process_2017	http://www.geneontology.org/	3166	d9da4dba4a3eb84d4a28a3835c06dfbbe5811f92
8002	143	GO_Cellular_Component_2017b	http://www.geneontology.org/	816	ecf39c41fa5bc7deb625a2b5761a708676e9db7c
10089	45	GO_Molecular_Function_2017b	http://www.geneontology.org/	3271	8d8340361dd36a458f1f0a401f1a3141de1f3200
13247	49	GO_Biological_Process_2017b	http://www.geneontology.org/	10125	6404c38bffc2b3732de4e3fbe417b5043009fe34
21809	2316	ARCHS4_Tissues	http://amp.pharm.mssm.edu/archs4	108	4126374338235650ab158ba2c61cd2e2383b70df
23601	2395	ARCHS4_Cell-lines	http://amp.pharm.mssm.edu/archs4	125	5496ef9c9ae9429184d0b9485c23ba468ee522a8
20883	299	ARCHS4_IDG_Coexp	http://amp.pharm.mssm.edu/archs4	352	ce60be284fdd5a9fc6240a355421a9e12b1ee84a
19612	299	ARCHS4_Kinases_Coexp	http://amp.pharm.mssm.edu/archs4	498	6721c5ed97b7772e4a19fdc3f797110df0164b75
25983	299	ARCHS4_TFs_Coexp	http://amp.pharm.mssm.edu/archs4	1724	8a468c3ae29fa68724f744cbef018f4f3b61c5ab
19500	137	SysMyo_Muscle_Gene_Sets	http://sys-myo.rhcloud.com/	1135
14893	128	miRTarBase_2017	http://mirtarbase.mbc.nctu.edu.tw/	3240	6b7c7fe2a97b19aecbfba12d8644af6875ad99c4
17598	1208	TargetScan_microRNA_2017	http://www.targetscan.org/	683	79d13fb03d2fa6403f9be45c90eeda0f6822e269
5902	109	Enrichr_Libraries_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	121	e9b7d8ee237d0a690bd79d970a23a9fa849901ed
12486	299	Enrichr_Submissions_TF-Gene_Coocurrence	http://amp.pharm.mssm.edu/Enrichr	1722	be2ca8ef5a8c8e17d7e7bd290e7cbfe0951396c0
1073	100	Data_Acquisition_Method_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	12	17ce5192b9eba7d109b6d228772ea8ab222e01ef
19513	117	DSigDB	http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/	4026	287476538ab98337dbe727b3985a436feb6d192a
14433	36	GO_Biological_Process_2018	http://www.geneontology.org/	5103	b5b77681c46ac58cd050e60bcd4ad5041a9ab0a9
8655	61	GO_Cellular_Component_2018	http://www.geneontology.org/	446	e9ebe46188efacbe1056d82987ff1c70218fa7ae
11459	39	GO_Molecular_Function_2018	http://www.geneontology.org/	1151	79ff80ae9a69dd00796e52569e41422466fa0bee
19741	270	TF_Perturbations_Followed_by_Expression	http://www.ncbi.nlm.nih.gov/geo/	1958	34d08a4878c19584aaf180377f2ea96faa6a6eb1
27360	802	Chromosome_Location_hg19	http://hgdownload.cse.ucsc.edu/downloads.html	36	fdab39c467ba6b0fb0288df1176d7dfddd7196d5
13072	26	NIH_Funded_PIs_2017_Human_GeneRIF	https://www.ncbi.nlm.nih.gov/pubmed/	5687	859b100fac3ca774ad84450b1fbb65a78fcc6b12
13464	45	NIH_Funded_PIs_2017_Human_AutoRIF	https://www.ncbi.nlm.nih.gov/pubmed/	12558	fc5bf033b932cf173633e783fc8c6228114211f8
13787	200	Rare_Diseases_AutoRIF_ARCHS4_Predictions	https://amp.pharm.mssm.edu/geneshot/	3725	375ff8cdd64275a916fa24707a67968a910329bb
13929	200	Rare_Diseases_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244	0f7fb7f347534779ecc6c87498e96b5460a8d652
16964	200	NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	12558	f77de51aaf0979dd6f56381cf67ba399b4640d28
17258	200	NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	5684	25fa899b715cd6a9137f6656499f89cd25144029
10352	58	Rare_Diseases_GeneRIF_Gene_Lists	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244	0fb9ac92dbe52024661c088f71a1134f00567a8b
10471	76	Rare_Diseases_AutoRIF_Gene_Lists	https://amp.pharm.mssm.edu/geneshot/	3725	ee3adbac2da389959410260b280e7df1fd3730df
12419	491	SubCell_BarCode	http://www.subcellbarcode.org/	104	b50bb9480d8a77103fb75b331fd9dd927246939a
19378	37	GWAS_Catalog_2019	https://www.ebi.ac.uk/gwas	1737	fef3864bcb5dd9e60cee27357eff30226116c49b
6201	45	WikiPathways_2019_Human	https://www.wikipathways.org/	472	b0c9e9ebb9014f14561e896008087725a2db24b7
4558	54	WikiPathways_2019_Mouse	https://www.wikipathways.org/	176	e7750958da20f585c8b6d5bc4451a5a4305514ba
3264	22	TRRUST_Transcription_Factors_2019	https://www.grnpedia.org/trrust/	571	5f8cf93e193d2bcefa5a37ccdf0eefac576861b0
7802	92	KEGG_2019_Human	https://www.kegg.jp/	308	3477bc578c4ea5d851dcb934fe2a41e9fd789bb4
8551	98	KEGG_2019_Mouse	https://www.kegg.jp/	303	187eb44b2d6fa154ebf628eba1f18537f64e797c
12444	23	InterPro_Domains_2019	https://www.ebi.ac.uk/interpro/	1071	18dd5ec520fdf589a93d6a7911289c205e1ddf22
9000	20	Pfam_Domains_2019	https://pfam.xfam.org/	608	a6325ed264f9ac9e6518796076c46a1d885cca7a
7744	363	DepMap_WG_CRISPR_Screens_Broad_CellLines_2019	https://depmap.org/	558	0b08b32b20854ac8a738458728a9ea50c2e04800
6204	387	DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019	https://depmap.org/	325	b7c4ead26d0eb64f1697c030d31682b581c8bb56
13420	32	MGI_Mammalian_Phenotype_Level_4_2019	http://www.informatics.jax.org/	5261	f1bed632e89ebc054da44236c4815cdce03ef5ee
14148	122	UK_Biobank_GWAS_v1	https://www.ukbiobank.ac.uk/tag/gwas/	857	958fb52e6215626673a5acf6e9289a1b84d11b4a
9813	49	BioPlanet_2019	https://tripod.nih.gov/bioplanet/	1510	e110851dfc763d30946f2abedcc2cd571ac357a0
1397	13	ClinVar_2019	https://www.ncbi.nlm.nih.gov/clinvar/	182	0a95303f8059bec08836ecfe02ce3da951150547
9116	22	PheWeb_2019	http://pheweb.sph.umich.edu/	1161	6a7c7321b6b72c5285b722f7902d26a2611117cb
17464	63	DisGeNET	https://www.disgenet.org	9828	3c261626478ce9e6bf2c7f0a8014c5e901d43dc0
394	73	HMS_LINCS_KinomeScan	http://lincs.hms.harvard.edu/kinomescan/	148	47ba06cdc92469ac79400fc57acd84ba343ba616
11851	586	CCLE_Proteomics_2020	https://portals.broadinstitute.org/ccle	378	7094b097ae2301a1d6a5bd856a193b084cca993d
8189	421	ProteomicsDB_2020	https://www.proteomicsdb.org/	913	8c87c8346167bac2ba68195a32458aba9b1acfd1
18704	100	lncHUB_lncRNA_Co-Expression	https://amp.pharm.mssm.edu/lnchub/	3729	45b597d7efa5693b7e4172b09c0ed2dda3305582
5605	39	Virus-Host_PPI_P-HIPSTer_2020	http://phipster.org/	6715	a592eed13e8e9496aedbab63003b965574e46a65
5718	31	Elsevier_Pathway_Collection	http://www.transgene.ru/disease-pathways/	1721	9196c760e3bcae9c9de1e3f87ad81f96bde24325
14156	40	Table_Mining_of_CRISPR_Studies		802	ad580f3864fa8ff69eaca11f6d2e7f9b86378d08
16979	295	COVID-19_Related_Gene_Sets	https://amp.pharm.mssm.edu/covid19	205	72b0346849570f66a77a6856722601e711596cb4
4383	146	MSigDB_Hallmark_2020	https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp	50	6952efda94663d4bd8db09bf6eeb4e67d21ef58c
54974	483	Enrichr_Users_Contributed_Lists_2020	https://maayanlab.cloud/Enrichr	1482	8dc362703b38b30ac3b68b6401a9b20a58e7d3ef
12118	448	TG_GATES_2020	https://toxico.nibiohn.go.jp/english/	1190	9e32560437b11b4628b00ccf3d584360f7f7daee
12361	124	Allen_Brain_Atlas_10x_scRNA_2021	https://portal.brain-map.org/	766	46f8235cb585829331799a71aec3f7c082170219
9763	139	Descartes_Cell_Types_and_Tissue_2021	https://descartes.brotmanbaty.org/bbi/human-gene-expression-during-development/	172
8078	102	KEGG_2021_Human	https://www.kegg.jp/	320
7173	43	WikiPathway_2021_Human	https://www.wikipathways.org/	622
5833	100	HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression	https://hubmapconsortium.github.io/ccf-asct-reporter/	344
14937	33	GO_Biological_Process_2021	http://www.geneontology.org/	6036
11497	80	GO_Cellular_Component_2021	http://www.geneontology.org/	511
11936	34	GO_Molecular_Function_2021	http://www.geneontology.org/	1274
9767	33	MGI_Mammalian_Phenotype_Level_4_2021	http://www.informatics.jax.org/	4601
14167	80	CellMarker_Augmented_2021	http://biocc.hrbmu.edu.cn/CellMarker/	1097
17851	102	Orphanet_Augmented_2021	http://www.orphadata.org/	3774
16853	360	COVID-19_Related_Gene_Sets_2021	https://maayanlab.cloud/covid19/	478
6654	136	PanglaoDB_Augmented_2021	https://panglaodb.se/	178
1683	10	Azimuth_Cell_Types_2021	https://azimuth.hubmapconsortium.org/	341
20414	112	PhenGenI_Association_2021	https://www.ncbi.nlm.nih.gov/gap/phegeni	950

markers_enriched=list()

if (markers_found) {
  # Upregulated markers
  
  # Convert Seurat names of upregulated marker per cluster to Entrez; use named lists for translation
  # Is that still neccessary?
  marker_genesets_up = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$up %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  # Tests done by Enrichr
  marker_genesets_up_enriched = purrr::map(marker_genesets_up, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  marker_genesets_up_enriched = purrr::map(list_names(marker_genesets_up_enriched), function(n) {
    return(purrr::map(marker_genesets_up_enriched[[n]], function(d){
      return(cbind(d, Cluster=rep(n, nrow(d)), Direction=rep("up", nrow(d))))
    }))
  })

  # Write to files
  invisible(purrr::map(names(marker_genesets_up_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_up_enriched[[n]],
                        file=file.path(param$path_out, "marker_degs", paste0("functions_marker_up_cluster_", n, "_vs_rest.xlsx")))
  }))
  
  
  # Downregulated markers
  
  # Convert Seurat names of downregulated marker per cluster to Entrez; use named lists for translation
  # Is that still neccessary?
  marker_genesets_down = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$down %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(x) seurat_rowname_to_entrez[[x]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  #  Tests done by Enrichr
  marker_genesets_down_enriched = purrr::map(marker_genesets_down, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  marker_genesets_down_enriched = purrr::map(list_names(marker_genesets_down_enriched), function(n) {
    return(purrr::map(marker_genesets_down_enriched[[n]], function(d){
      return(cbind(d, Cluster=rep(n, nrow(d)), Direction=rep("down", nrow(d))))
    }))
  })
  
  # Write to files
  invisible(purrr::map(names(marker_genesets_down_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_down_enriched[[n]],
                        file=file.path(param$path_out, "marker_degs", paste0("functions_marker_down_cluster_", n, "_vs_rest.xlsx")))
  }))
  
  # Combine, flatten into data.frame and add to sc misc slot
  marker_genesets_enriched = c(marker_genesets_up_enriched, marker_genesets_down_enriched)
  marker_genesets_enriched = unname(marker_genesets_enriched)
  marker_genesets_enriched = purrr::map(marker_genesets_enriched, FlattenEnrichr) %>% dplyr::bind_rows()
  marker_genesets_enriched$Cluster = factor(marker_genesets_enriched$Cluster, levels=levels(sc$seurat_clusters))
  marker_genesets_enriched$Direction = factor(marker_genesets_enriched$Direction, levels=c("up", "down"))
  
  misc_content = Misc(sc, "markers")
  misc_content[["enrichr"]] = marker_genesets_enriched
  suppressWarnings({Misc(sc, "markers") = misc_content})
}

The following table contains the top enriched terms per cluster.

# Top enriched terms (TODO: better plots, functions)
if (markers_found) {
  cat('#### {.tabset} \n \n')
  
  # Get top ten up and down over all databases per cluster
  marker_genesets_top_enriched = marker_genesets_enriched %>% dplyr::group_by(Cluster, Direction) %>%
    dplyr::top_n(n=10, wt=Combined.Score)
  
  # Print as tabs
  for(n in levels(marker_genesets_top_enriched$Cluster)){
    cat('##### ', n, ' \n')
    
    print(knitr::kable(marker_genesets_top_enriched %>% dplyr::ungroup() %>% dplyr::filter(Cluster==n) %>% dplyr::select(Database, Term, Direction, Adjusted.P.value, Odds.Ratio, Combined.Score), 
                     align="l", caption="Top ten enriched terms per geneset", format="html") %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%", height="700px"))

    cat(' \n \n')
  }
  cat(' \n \n')
}

1

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
GO_Biological_Process_2018	SRP-dependent cotranslational protein targeting to membrane (GO:0006614)	up	0.0000000	339.85854	10701.7079
GO_Biological_Process_2018	cotranslational protein targeting to membrane (GO:0006613)	up	0.0000000	323.98605	10099.0276
GO_Biological_Process_2018	protein targeting to ER (GO:0045047)	up	0.0000000	309.52444	9554.2490
GO_Biological_Process_2018	viral gene expression (GO:0019080)	up	0.0000000	270.28155	8098.5111
GO_Biological_Process_2018	nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)	up	0.0000000	265.10667	7909.1919
GO_Biological_Process_2018	viral transcription (GO:0019083)	up	0.0000000	262.59245	7817.4476
GO_Biological_Process_2018	nuclear-transcribed mRNA catabolic process (GO:0000956)	up	0.0000000	166.16407	4435.6916
GO_Biological_Process_2018	peptide biosynthetic process (GO:0043043)	up	0.0000000	166.16407	4435.6916
GO_Cellular_Component_2018	cytosolic ribosome (GO:0022626)	up	0.0000000	237.77265	6920.4432
GO_Cellular_Component_2018	cytosolic part (GO:0044445)	up	0.0000000	182.70000	4993.9420
GO_Cellular_Component_2018	ribosome (GO:0005840)	up	0.0000000	284.54286	7633.6437
GO_Biological_Process_2018	positive regulation of myeloid leukocyte mediated immunity (GO:0002888)	down	0.0001144	231.95349	3217.1432
GO_Biological_Process_2018	positive regulation of superoxide anion generation (GO:0032930)	down	0.0001280	173.94767	2295.7382
GO_Biological_Process_2018	positive regulation of leukocyte degranulation (GO:0043302)	down	0.0001280	173.94767	2295.7382
GO_Biological_Process_2018	regulation of superoxide anion generation (GO:0032928)	down	0.0001935	139.14419	1760.3186
GO_Biological_Process_2018	macrophage activation (GO:0042116)	down	0.0004062	99.36877	1171.6949
GO_Biological_Process_2018	granzyme-mediated apoptotic signaling pathway (GO:0008626)	down	0.0024849	226.70455	2146.7751
GO_Biological_Process_2018	regulation of inositol phosphate biosynthetic process (GO:0010919)	down	0.0024849	226.70455	2146.7751
GO_Biological_Process_2018	microglial cell activation (GO:0001774)	down	0.0030634	181.35455	1656.5788
GO_Biological_Process_2018	defense response to protozoan (GO:0042832)	down	0.0036513	151.12121	1337.1595
GO_Biological_Process_2018	protein kinase C signaling (GO:0070528)	down	0.0049990	100.73232	823.7409

2

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
GO_Molecular_Function_2018	MHC protein binding (GO:0042287)	up	0.0225889	43.15368	288.2955
GO_Biological_Process_2018	regulation of immune response (GO:0050776)	up	0.0000031	24.04755	443.7888
GO_Biological_Process_2018	regulation of phosphatidylinositol 3-kinase signaling (GO:0014066)	up	0.0014975	33.76740	384.6143
GO_Biological_Process_2018	leukocyte cell-cell adhesion (GO:0007159)	up	0.0014975	77.89453	870.8543
GO_Biological_Process_2018	granzyme-mediated apoptotic signaling pathway (GO:0008626)	up	0.0031016	302.43939	3030.9316
GO_Biological_Process_2018	cellular defense response (GO:0006968)	up	0.0068534	35.22170	315.7909
GO_Biological_Process_2018	lymphocyte mediated immunity (GO:0002449)	up	0.0074527	134.38384	1172.8783
GO_Biological_Process_2018	response to prostaglandin E (GO:0034695)	up	0.0078167	120.93939	1033.6205
GO_Biological_Process_2018	receptor clustering (GO:0043113)	up	0.0286757	54.93939	391.6646
GO_Cellular_Component_2018	T cell receptor complex (GO:0042101)	up	0.0094919	86.36797	686.8978
GO_Biological_Process_2018	regulation of peptidase activity (GO:0052547)	down	0.0046189	155.98438	1413.4487
GO_Biological_Process_2018	cellular response to hormone stimulus (GO:0032870)	down	0.0461490	31.89744	193.5089
GO_Cellular_Component_2018	tertiary granule lumen (GO:1904724)	down	0.0008889	76.65385	848.4517
GO_Cellular_Component_2018	ficolin-1-rich granule lumen (GO:1904813)	down	0.0032894	33.10333	286.7238
GO_Cellular_Component_2018	ribonucleoprotein granule (GO:0035770)	down	0.0193514	31.89744	193.5089
GO_Cellular_Component_2018	multivesicular body lumen (GO:0097486)	down	0.0325619	195.84314	992.8793
GO_Cellular_Component_2018	late endosome lumen (GO:0031906)	down	0.0338429	146.86765	707.7996
GO_Cellular_Component_2018	MHC class II protein complex (GO:0042613)	down	0.0396799	90.35747	395.7287
GO_Cellular_Component_2018	lamellar body (GO:0042599)	down	0.0402595	83.89916	361.6911
GO_Cellular_Component_2018	MHC protein complex (GO:0042611)	down	0.0449307	69.08304	285.3111

3

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
GO_Molecular_Function_2018	Toll-like receptor binding (GO:0035325)	up	0.0007688	57.14493	703.7553
GO_Biological_Process_2018	neutrophil activation involved in immune response (GO:0002283)	up	0.0000000	17.31707	2244.4778
GO_Biological_Process_2018	neutrophil degranulation (GO:0043312)	up	0.0000000	16.77640	2089.5871
GO_Biological_Process_2018	neutrophil mediated immunity (GO:0002446)	up	0.0000000	16.44540	2028.2054
GO_Biological_Process_2018	regulation of mast cell degranulation (GO:0043304)	up	0.0000002	63.17949	1344.6011
GO_Biological_Process_2018	positive regulation of viral entry into host cell (GO:0046598)	up	0.0001673	71.43478	920.9359
GO_Biological_Process_2018	regulation of leukocyte degranulation (GO:0043300)	up	0.0002638	57.14493	703.7553
GO_Biological_Process_2018	regulation of lymphocyte apoptotic process (GO:0070228)	up	0.0015487	71.18051	701.2927
GO_Biological_Process_2018	cellular response to bacterial lipopeptide (GO:0071221)	up	0.0015487	71.18051	701.2927
GO_Cellular_Component_2018	ficolin-1-rich granule (GO:0101002)	up	0.0000000	15.92191	875.7980
GO_Cellular_Component_2018	tertiary granule (GO:0070820)	up	0.0000000	15.25464	722.2744
GO_Molecular_Function_2018	T cell receptor binding (GO:0042608)	down	0.0006884	416.04167	4420.5084
GO_Biological_Process_2018	SRP-dependent cotranslational protein targeting to membrane (GO:0006614)	down	0.0000000	157.39715	6176.8613
GO_Biological_Process_2018	cotranslational protein targeting to membrane (GO:0006613)	down	0.0000000	149.78163	5809.0820
GO_Biological_Process_2018	protein targeting to ER (GO:0045047)	down	0.0000000	142.86638	5478.0789
GO_Biological_Process_2018	viral gene expression (GO:0019080)	down	0.0000000	124.21250	4600.5061
GO_Biological_Process_2018	nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)	down	0.0000000	121.76471	4487.1395
GO_Biological_Process_2018	viral transcription (GO:0019083)	down	0.0000000	120.57646	4432.2667
GO_Cellular_Component_2018	cytosolic ribosome (GO:0022626)	down	0.0000000	108.88158	3898.0371
GO_Cellular_Component_2018	T cell receptor complex (GO:0042101)	down	0.0000000	432.09957	10916.2400
GO_Cellular_Component_2018	cytosolic small ribosomal subunit (GO:0022627)	down	0.0000000	139.05349	3348.1104

Differentially expressed genes

If requested, we identify genes that are differentially expressed between two groups of cells. Groups can be defined by columns in the cell metadata. Different types of tests can be used and input data for testing can be the different assays as well as the computed dimensionality reductions. Resulting p-values are adjusted using the Bonferroni method. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

# We first compute the DEGs for all requested contrasts

# Prepare a list with contrasts (input can be R data.frame table or Excel file)
degs_contrasts_list = DegsSetupContrastsList(sc, param$deg_contrasts, param$latent_vars)

# Add the actual data to the list
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){
  # If there were already errors, just return
  if (length(contrast[["error_messages"]]) > 0) return(c(contrast, list(object=NULL, cells_group1_idx_subset=as.integer(), cells_group2_idx_subset=as.integer())))
  
  # Get cells indices
  cells_group1_idx = contrast[["cells_group1_idx"]]
  cells_group2_idx = contrast[["cells_group2_idx"]]

  # Create object
  if (contrast[["use_reduction"]]) {
    # Use dimensionality reduction
    contrast[["object"]] = Seurat::Reductions(sc, slot=contrast[["assay"]])
  } else {
    # Use assay
    contrast[["object"]] = Seurat::GetAssay(sc[,unique(c(cells_group1_idx, cells_group2_idx))], assay=contrast[["assay"]])
    
    # This saves a lot of memory for parallelisation
    if (contrast[["slot"]]!="scale.data") contrast[["object"]]@scale.data = new(Class='matrix')
  }
  
  # Variable latent vars must be passed as data.frame
  if (!is.null(contrast[["latent_vars"]]) && length(contrast[["latent_vars"]]) > 0) {
    contrast[["latent_vars"]] = sc[[unique(c(cells_group1_idx, cells_group2_idx)), contrast[["latent_vars"]], drop=FALSE]]
  }

  # Now update cell indices so that they match to subset
  contrast[["cells_group1_idx_subset"]] = match(colnames(sc)[cells_group1_idx], colnames(contrast[["object"]]))
  contrast[["cells_group2_idx_subset"]] = match(colnames(sc)[cells_group2_idx], colnames(contrast[["object"]]))
  
  return(contrast)
})

# Compute the tests
# TODO: this chunk may be done in parallel in future
degs_contrasts_results = purrr::map(degs_contrasts_list, function(contrast) {
  if (length(contrast$error_messages)==0) {
    # No errors, do contrast
    test_results = suppressWarnings(
      DegsTestCellSets(object=contrast[["object"]],
                       slot=contrast[["slot"]],
                       cells_1=colnames(contrast[["object"]])[contrast[["cells_group1_idx_subset"]]],
                       cells_2=colnames(contrast[["object"]])[contrast[["cells_group2_idx_subset"]]],
                       is_reduction=contrast[["use_reduction"]],
                       logfc.threshold=contrast[["log2FC"]],
                       test.use=contrast[["test"]],
                       min.pct=contrast[["min_pct"]],
                       latent.vars=contrast[["latent_vars"]])
    )
  } else {
    # Errors, return empty data.frame
    test_results = DegsEmptyResultsTable()
  }
  
  # Sort and filter table
  test_results = test_results %>% DegsSort() %>% DegsFilter(contrast[["log2FC"]], contrast[["padj"]], split_by_dir=FALSE)

  # Add mean gene expression data (counts or data, dep on slot)
  avg.1 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group1_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  avg.2 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group2_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  test_results = cbind(test_results, avg.1, avg.2)
  
  # Add test results and drop unneccessary data
  contrast = c(contrast, list(results=test_results))
  contrast[["object"]] = NULL
  contrast[["cells_group1_idx_subset"]] = NULL
  contrast[["cells_group2_idx_subset"]] = NULL
  
  return(contrast)
})

# Also remove objects from deg_contrasts_list (save memory)
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){ contrast[["object"]] = NULL; return(contrast)})

# Use the existing variable and add Enrichr results
# Not in parallel due to server load
degs_contrasts_results = purrr::map(degs_contrasts_results, function(contrast) {
  # Get results table
  results_table = contrast$results
  
  # Drop existing results
  if ("enrichr" %in% names(contrast)) contrast[["enrichr"]] = NULL
  
  # Split into up- and downregulated DEGs, then translate to Entrez gene, runEnrichr
  degs_up = dplyr::filter(results_table, avg_log2FC > 0) %>% dplyr::pull(gene) %>% unique()
  degs_up = sapply(degs_up, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_up = degs_up[!is.na(degs_up)]
  enrichr_results_up = EnrichrTest(genes=degs_up, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  degs_down = dplyr::filter(results_table, avg_log2FC < 0) %>% dplyr::pull(gene) %>% unique()
  degs_down = sapply(degs_down, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_down = degs_down[!is.na(degs_down)]
  enrichr_results_down = EnrichrTest(genes=degs_down, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  # Flatten both enrichr results into tables
  enrichr_results_up = purrr::map_dfr(names(enrichr_results_up), function(n) {
    return(cbind(enrichr_results_up[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_up[[n]])), levels=names(enrichr_results_up)), 
               Direction=factor(rep("up", nrow(enrichr_results_up[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  enrichr_results_down = purrr::map_dfr(names(enrichr_results_down), function(n) {
    return(cbind(enrichr_results_down[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_down[[n]])), levels=names(enrichr_results_down)), 
               Direction=factor(rep("up", nrow(enrichr_results_down[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  # Rbind and add factor levels
  enrichr_results = rbind(enrichr_results_up, enrichr_results_down)
  return(c(contrast, list(enrichr=enrichr_results)))
})

# Now regroup list so that subsets are together again
original_contrast_rows = purrr::map_int(degs_contrasts_results, function(contrast){ return(contrast[["contrast_row"]]) })
degs = split(degs_contrasts_results, original_contrast_rows)

# Write degs to files
invisible(purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = file.path(param$path_out, "marker_degs", paste0("degs_contrast_row_", degs_subsets[[1]][["contrast_row"]], "_results.xlsx"))
  
  # Write degs
  degs_subsets_results = purrr::map(degs_subsets, function(contrast) {return(contrast[["results"]])})
  names(degs_subsets_results) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = DegsWriteToFile(degs_subsets_results, 
                         annot_ensembl=annot_ensembl,
                         gene_to_ensembl=seurat_rowname_to_ensembl,
                         file=file,
                         additional_readme=NULL)
  
  return(file)
}))


invisible(purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = file.path(param$path_out, "marker_degs", paste0("degs_contrast_row_", degs_subsets[[1]][["contrast_row"]],  "_functions.xlsx"))
  
  # Write Enrichr results
  degs_subsets_enrichr = purrr::map(degs_subsets, function(contrast) {return(contrast[["enrichr"]])})
  names(degs_subsets_enrichr) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = EnrichrWriteResults(degs_subsets_enrichr, file=file)
  
  return(file)
}))

# Add to sc object
Misc(sc, "degs") = degs

for (i in seq(degs)) { 
  for (j in seq(degs[[i]])) {

    # Average expression of all genes
    x = subset(sc, cells=degs[[i]][[j]]$cells_group2_idx) %>% GetAssayData(slot="data")
    x = log2(Matrix::rowMeans(expm1(x)) + 1)
    y = subset(sc, cells=degs[[i]][[j]]$cells_group1_idx) %>% GetAssayData(slot="data") 
    y = log2(Matrix::rowMeans(expm1(y)) + 1)
    sc_avg_log2FC = data.frame(x, y, col="none", gene=rownames(sc))
    lims = c(min(c(x, y)), max(x, y))
    
    ## Color DEGs
    up = degs[[i]][[j]]$results %>% dplyr::filter(avg_log2FC > 0) %>% dplyr::pull(gene)
    if (length(up) > 0) sc_avg_log2FC[up, "col"] = "up"
    down = degs[[i]][[j]]$results %>% dplyr::filter(avg_log2FC < 0) %>% dplyr::pull(gene)
    if (length(down) > 0) sc_avg_log2FC[down, "col"] = "down"

    ## Plots
    degs[[i]][[j]]$plot_scatter = ggplot(sc_avg_log2FC %>% dplyr::arrange(col, gene), aes(x=x, y=y, col=col)) + 
      geom_abline(slope=1, intercept=0, col="lightgrey") + 
      geom_abline(slope=1, intercept=c(-degs[[i]][[j]]$log2FC, degs[[i]][[j]]$log2FC), col="lightgrey", lty=2) + 
      geom_point() + 
      xlim(lims) + ylim(lims) +
      AddStyle(ylab=degs[[i]][[j]]$condition_group1, xlab=degs[[i]][[j]]$condition_group2, 
               col=c(none="grey", up="darkgoldenrod1", down="steelblue"), 
               legend_position="bottom", legend_title="Filtered genes")

    # Feature plot of top 4 genes, sorted by the p-value
    degs_top = degs[[i]][[j]]$results %>% dplyr::top_n(n=-4, wt=p_val) %>% dplyr::top_n(n=-4, wt=avg_log2FC) %>% dplyr::pull(gene)
    if (length(degs_top) > 0) {
      p_list = Seurat::FeaturePlot(sc, features=degs_top, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
      for (p in seq(p_list)) p_list[[p]] = p_list[[p]] + AddStyle(legend_position="bottom", xlab="", ylab="")
      degs[[i]][[j]]$plot_feature = patchwork::wrap_plots(p_list, ncol=ifelse(length(degs_top) > 1, 2, 1))
    } else {
      degs[[i]][[j]]$plot_feature = NULL
    }
  }
}

knitr_header_string = '

## {{condition_column}}: {{condition_group1}} vs {{condition_group2}}

General configuration:

* assay: {{assay}}
* slot: {{slot}}
* test: {{test}}
* maximum adjusted p-value: {{padj}}
* minimum absolute log2 foldchange: {{log2FC}}
* minimum percentage of cells: {{min_pct}}
* latent vars: {{latent_vars}}

Subset on column: \'{{subset_column}}\''

if (length(degs)==0) message("No DEG contrasts specified.")

for (i in seq(degs)) {
  # Get subsets
  degs_subsets = degs[[i]]
  first_contrast = degs_subsets[[1]]
  
  # Create header
  cat(
    knitr::knit_expand(text=knitr_header_string,
                     condition_column=first_contrast[["condition_column"]],
                     condition_group1=first_contrast[["condition_group1"]],
                     condition_group2=first_contrast[["condition_group2"]],
                     assay=first_contrast[["assay"]],
                     slot=first_contrast[["slot"]],
                     test=first_contrast[["test"]],
                     padj=first_contrast[["padj"]],
                     log2FC=first_contrast[["log2FC"]],
                     min_pct=first_contrast[["min_pct"]],
                     latent_vars=ifelse(!is.null(first_contrast[["latent_vars"]]), paste(colnames(first_contrast[["latent_vars"]]), collapse=","), "-"),
                     subset_column=ifelse(is.na(first_contrast[["subset_column"]]), "-", first_contrast[["subset_column"]]))
  , '\n')
  
  # Get error messages
  error_messages = unique(purrr::flatten_chr(purrr::map(degs_subsets, function(contrast){return(contrast[["error_messages"]])})))

   # Create combined results table
  degs_subsets_results = purrr::map_dfr(degs_subsets, function(contrast) {
    subset_group_value = ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All")
    return(contrast[["results"]] %>% 
      dplyr::summarise(subset_group=subset_group_value,
                       Cells1=length(contrast[["cells_group1_idx"]]),
                       Cells2=length(contrast[["cells_group2_idx"]]),
                       DEGs=length(gene),
                       DEGs_up=sum(avg_log2FC > 0),
                       DEGs_down=sum(avg_log2FC < 0)))
  }) %>% tibble::as_tibble()
  
  # Print warnings/errors
  if (length(error_messages) > 0) {
    warning(error_messages)
  }
  
  # Print summary table
  print(
      knitr::kable(degs_subsets_results,
                   align="l", caption="DEG summary", 
                   col.names=c("Subset", "Cells in group 1", "Cells in group 2", "# DEGs", "# DEGs upregulated", "# DEGs downregulated"), 
                   format="html") %>%
        kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
    )
  
  # Print plots per contrast
  for (contrast in seq(degs_subsets)) {
    p = degs_subsets[[contrast]]$plot_scatter | degs_subsets[[contrast]]$plot_feature
    title = "Scatterplot and feature plots"
    if (!is.na(degs_subsets[[contrast]]$subset_column)) {
      title = paste0(title, " (subset ", degs_subsets[[contrast]]$subset_column, 
                     ": ", degs_subsets[[contrast]]$subset_group, ")")
    }
    
    p = p + patchwork::plot_annotation(title=title)
    print(p)
  }
  
  cat('\n \n')
} 

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0.584962500721156
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘-’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
All	89	100	361	229	132

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0.584962500721156
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	46	44	177	133	44
2	33	40	148	103	45
3	10	16	2	2	0

Phase: G1 vs G2M

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0.584962500721156
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	26	30	0	0	0
2	21	25	0	0	0

Output

# Add colour lists for orig.dataset
col = GenerateColours(num_colours=length(levels(sc$orig.dataset)), names=levels(sc$orig.dataset), palette=param$col_palette_samples, alphas=1)
sc = ScAddLists(sc, lists=list(orig.dataset=col), lists_slot="colour_lists")

# Add experiment details
Seurat::Misc(sc, "experiment") = list(project_id=param$project_id, date=Sys.Date(), species=gsub("_gene_ensembl", "", param$mart_dataset))

# Add parameter
Seurat::Misc(sc, "parameters") = param

# Add technical output
session_info = suppressMessages(sessioninfo::session_info())
Seurat::Misc(sc, "technical") = list(scrnaseq_git=GitRepositoryVersion(param$path_to_git),
                                     R=session_info$platform$version,
                                     packages=as.data.frame(session_info$packages)[, c("package", "ondiskversion", "loadedversion", "date")])

Export to Loupe Cell Browser

If all provided datasets are of type “10x,” we export the UMAP 2D visualisation, metadata such as the cell clusters, and lists of differentially expressed genes, so you can open and work with these in the Loupe Cell Browser https://support.10xgenomics.com/single-cell-gene-expression/software/visualization/latest/what-is-loupe-cell-browser.

if (all(param$path_data$type == "10x")) { 
  
  # Export reductions (umap, pca, others)
  for(r in Seurat::Reductions(sc)) {
    write.table(Seurat::Embeddings(sc, reduction=r)[,1:2] %>% as.data.frame() %>% tibble::rownames_to_column(var="Barcode"),
                file=file.path(param$path_out, "export", paste0("Loupe_projection_", r, ".csv")), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")
  }
  
  # Export categorical metadata
  loupe_meta = sc@meta.data
  idx_keep = sapply(1:ncol(loupe_meta), function(x) !is.numeric(loupe_meta[,x]))
  write.table(x=loupe_meta[, idx_keep] %>% tibble::rownames_to_column(var="barcode"), 
              file=file.path(param$path_out, "export", "Loupe_metadata.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

  # Export gene sets (CC genes, known markers, per-cluster markers up- and downregulated, ...)
  gene_lists = Misc(sc, "gene_lists")
  loupe_genesets = purrr::map_dfr(names(gene_lists), function(n) {return(data.frame(List=n, Name=gene_lists[[n]]))})
  loupe_genesets$Ensembl = seurat_rowname_to_ensembl[loupe_genesets$Name]
  write.table(loupe_genesets, file=file.path(param$path_out, "export", "Loupe_genesets.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")
}

Result files are:

• export/
  • Loupe_projection_(umap|pca|…).csv: Seurat UMAP/PCA/… projections for visualization
  • Loupe_metadata.csv: Seurat cell meta data including clusters and cell cycle phases
  • Loupe_genesets.csv: Gene sets such as markers, DEGs, cell cycles

Projections can be imported in Loupe via “Import Projection,” cell meta data via “Import Categories” and gene sets via “Import Lists”

Export to cellxgene browser

We export the assay data, cell metadata, clustering and visualisation in a format that can be read by the cellxgene browser https://github.com/chanzuckerberg/cellxgene.

# Convert Seurat single cell object to python anndata object which will be accessible via reticulate here
adata = sceasy::convertFormat(sc, from="seurat", to="anndata", outFile=NULL, assay=DefaultAssay(sc))

# Set up correct colours (see https://chanzuckerberg.github.io/cellxgene/posts/prepare) so that colours match
adata$uns = dict(
  seurat_clusters_colors=np_array(unname(Misc(sc, "colour_lists")$seurat_clusters), dtype='<U7'), 
  orig.ident_colors=np_array(unname(Misc(sc, "colour_lists")$orig.ident), dtype='<U7'),
  orig.dataset_colors=np_array(unname(Misc(sc, "colour_lists")$orig.dataset), dtype='<U7'),
  Phase_colors=np_array(unname(Misc(sc, "colour_lists")$Phase), dtype='<U7')
)

# Write to h5ad file
adata$write(file.path(param$path_out, "export", "sc.h5ad"), compression='gzip')

Result files are:

• export/
  • sc.h5ad: H5AD object for cellxgene browser

Copy/upload to data directory of your cellxgene browser

Export to the Cerebro browser

We export the assay data, clustering, visualisation, marker genes, enriched pathways and degs in a format that can be read by the Cerebro Browser https://github.com/romanhaa/cerebroApp/.

# Export to cerebro
res = ExportToCerebro(sc=sc, 
                      path=file.path(param$path_out, "export", "sc.crb"), 
                      assay=DefaultAssay(sc),
                      delayed_array=FALSE)

Result files are:

• export/
  • sc.crb: Object for Cerebro browser

Load into Cerebro browser.

Other output files

# Seurat object
saveRDS(sc, file=file.path(param$path_out, "data", "sc.rds"))

# Counts (RNA)
invisible(ExportSeuratAssayData(sc, 
                      dir=file.path(param$path_out, "data", "counts"), 
                      assays="RNA", 
                      slot="counts",
                      include_cell_metadata_cols=colnames(sc[[]]),
                      metadata_prefix=paste0(param$project_id, ".")))

# Metadata
openxlsx::write.xlsx(x=sc[[]] %>% tibble::rownames_to_column(var="Barcode"), file=file.path(param$path_out, "data", "cell_metadata.xlsx"), row.names=FALSE, col.names=TRUE)

# Annotation as excel file
openxlsx::write.xlsx(x=data.frame(seurat_id=rownames(sc), ensembl_gene_id=seurat_rowname_to_ensembl[rownames(sc)]) %>%
                       dplyr::inner_join(annot_ensembl, by="ensembl_gene_id"),
                     file=file.path(param$path_out, "annotation", "gene_annotation.xlsx"), 
                     row.names=FALSE, col.names=TRUE)

# Data and annotation for a subset of 500 cells for Morpheus
cells_subset = ScSampleCells(sc, n=500, seed=1)
openxlsx::write.xlsx(Seurat::GetAssayData(sc[, cells_subset], slot="data") %>% as.data.frame() %>% tibble::rownames_to_column(var="gene"), 
                     file=file.path(param$path_out, "data", paste0("subset_", 500, "_cells_normalised_data.xlsx")),
                     row.names=FALSE, col.names=TRUE)
openxlsx::write.xlsx(Seurat::GetAssayData(sc[, cells_subset], slot="scale.data") %>% as.data.frame() %>% tibble::rownames_to_column(var="gene"), 
                     file=file.path(param$path_out, "data", paste0("subset_", 500, "_cells_scaled_data.xlsx")),
                     row.names=FALSE, col.names=TRUE)
openxlsx::write.xlsx(sc[[]][cells_subset, ] %>% as.data.frame() %>% tibble::rownames_to_column(var="cell"), 
           file=file.path(param$path_out, "data",paste0("subset_", 500, "_cells_column_annotations.xlsx")),
           row.names=FALSE, col.names=TRUE)

Result files are:

• annotation/
  • gene_annotation.xlsx: Ensembl annotation of the genes in Excel format
  • cell_cycle_markers.xlsx: Markers for cell cycle phases in Excel format
  • species_gene_ensembl.vEnsembl.annot.txt: Raw annotation downloaded from Ensembl as tab-separated file
• data/
  • sc.rds: Seurat object for import into R
  • cell_metadata.xlsx: Cell metadata in Excel format
  • counts: Raw counts of the entire dataset as sparse matrix
  • subset_500_cells_normalised_data.xlsx: Normalised subset of 500 cells in Excel format (for heatmap plotting with morpheus (https://software.broadinstitute.org/morpheus/))
  • subset_500_cells_scaled_data.xlsx: Normalised and scaled subset of 500 cells in Excel format (for heatmap plotting with morpheus)
  • subset_500_cells_column_annotations.xlsx: Cell annotations for the 500 cells subset in Excel format (for heatmap plotting with morpheus)
• figures/
  • all plots of the HTML report in PNG and PDF format
• marker_degs/
  • markers_cluster_vs_rest.xslx: Marker genes for each cluster in Excel format
  • functions_marker_up_cluster_[1,2,3,…]_vs_rest.xslx: Biological functions and pathways that were found enriched in the up-regulated markers for cluster 1,2,3,… in Excel format
  • functions_marker_down_cluster_[1,2,3,…]_vs_rest.xslx: Biological functions and pathways that were found enriched in the down-regulated markers for cluster 1,2,3,… in Excel format
  • degs_contrast_row_[1,2,3,…]_results.xlsx: Results (genes) of the DEG analysis specified in the first,second,third,… row of the configuration parameter (Excel format)
  • degs_contrast_row_[1,2,3,…]_functions.xlsx: Results (enriched functions and pathways) of the DEG analysis specified in the first,second,third,… row of the configuration parameter (Excel format)

Parameters and software versions

The following parameters were used to run the workflow.

out = ScrnaseqParamsInfo(params=param)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"), full_width=FALSE, position="left")

Name	Value
project_id	pbmc
path_data	name:pbmc_10x, pbmc_smartseq2; type:10x, smartseq2; path:test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/, test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/smartseq2/counts_table.tsv.gz; stats:NA, NA; suffix:-1, -2
downsample_cells_n	100
path_out	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/
file_known_markers	test_datasets/10x_SmartSeq2_pbmc_GSE132044/known_markers.xlsx
mart_dataset	hsapiens_gene_ensembl
annot_version	98
annot_main	ensembl=ensembl_gene_id, symbol=external_gene_name, entrez=entrezgene_accession
mart_attributes	ensembl_gene_id, external_gene_name, entrezgene_accession, chromosome_name, start_position, end_position, percentage_gene_gc_content, gene_biotype, strand, description
mt	^MT-
cell_filter	pbmc_10x:nFeature_RNA=c(200, NA), percent_mt=c(NA, 20); pbmc_smartseq2_sample1:nFeature_RNA=c(200, NA), percent_mt=c(NA, 20)
feature_filter	pbmc_10x:min_counts=1, min_cells=3; pbmc_smartseq2_sample1:min_counts=1, min_cells=3
samples_min_cells	10
norm	RNA
cc_remove	FALSE
cc_remove_all	FALSE
cc_rescore_after_merge	TRUE
integrate_samples	method:integrate; dimensions:30; reference:; use_reciprocal_pca:FALSE
pc_n	10
cluster_resolution	0.5
marker_padj	0.05
marker_log2FC	1
marker_pct	0.25
deg_contrasts	condition_column:orig.ident, orig.ident, Phase; condition_group1:pbmc_10x, pbmc_10x, G1; condition_group2:pbmc_smartseq2_sample1, pbmc_smartseq2_sample1, G2M; subset_column:NA, seurat_clusters, seurat_clusters; subset_group:NA, , 1;2; downsample_cells_n:NA, 50, 30; log2FC:0.584962500721156, 0.584962500721156, 0.584962500721156
enrichr_padj	0.05
enrichr_dbs	GO_Molecular_Function_2018, GO_Biological_Process_2018, GO_Cellular_Component_2018
col	palevioletred
col_palette_samples	ggsci::pal_jama
col_palette_clusters	ggsci::pal_igv
path_to_git	.
debugging_mode	default_debugging
file_annot	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results//annotation/hsapiens_gene_ensembl.v98.annot.txt
file_cc_genes	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results//annotation/cell_cycle_markers.xlsx
biomart_mirror	www
samples_to_drop
col_samples	pbmc_10x=#374E55FF, pbmc_smartseq2_sample1=#DF8F44FF
col_clusters	1=#5050FFFF, 2=#CE3D32FF, 3=#749B58FF

This report was created with generated using the scrnaseq GitHub repository. Software versions were collected at run time.

out = ScrnaseqSessionInfo(param$path_to_git)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))

Name	Version
ktrns/scrnaseq	97ba037829b16906dc0053fd1e09510f5ad29d13
R	R version 4.0.3 (2020-10-10)
Platform	x86_64-conda-linux-gnu (64-bit)
Operating system	Debian GNU/Linux 10 (buster)
Packages	abind1.4-5, annotate1.68.0, AnnotationDbi1.52.0, ape5.5, askpass1.1, assertthat0.2.1, babelgene21.4, bibtex0.4.2.3, Biobase2.50.0, BiocFileCache1.14.0, BiocGenerics0.36.0, BiocParallel1.24.1, biomaRt2.46.3, bit4.0.4, bit644.0.5, bitops1.0-7, blob1.2.1, bslib0.2.4, cachem1.0.4, cerebroApp1.3.1, cli3.0.0, cluster2.1.1, codetools0.2-18, colorspace2.0-2, colourpicker1.1.0, cowplot1.1.1, crayon1.4.1, curl4.3.2, data.table1.14.0, DBI1.1.1, dbplyr2.1.1, DelayedArray0.16.3, deldir0.2-10, digest0.6.27, dplyr1.0.5, DT0.17, ellipsis0.3.2, enrichR3.0, evaluate0.14, fansi0.5.0, farver2.1.0, fastmap1.1.0, fitdistrplus1.1-3, fs1.5.0, future1.21.0, future.apply1.7.0, generics0.1.0, GenomeInfoDb1.26.4, GenomeInfoDbData1.2.4, GenomicRanges1.42.0, ggplot23.3.5, ggrepel0.9.1, ggridges0.5.3, ggsci2.9, globals0.14.0, glue1.4.2, goftest1.2-2, graph1.68.0, gridExtra2.3, GSEABase1.52.1, GSVA1.38.2, gtable0.3.0, highr0.8, hms1.0.0, htmltools0.5.1.1, htmlwidgets1.5.3, httpuv1.5.5, httr1.4.2, ica1.0-2, igraph1.2.6, IRanges2.24.1, irlba2.3.3, jquerylib0.1.3, jsonlite1.7.2, kableExtra1.3.4, KernSmooth2.23-18, knitcitations1.0.12, knitr1.31, labeling0.4.2, later1.1.0.1, lattice0.20-41, lazyeval0.2.2, leiden0.3.8, lifecycle1.0.0, limma3.46.0, listenv0.8.0, lmtest0.9-38, lubridate1.7.10, magrittr2.0.1, MASS7.3-53.1, MAST1.16.0, Matrix1.3-4, MatrixGenerics1.2.1, matrixStats0.58.0, memoise2.0.0, mgcv1.8-34, mime0.11, miniUI0.1.1.1, msigdbr7.4.1, munsell0.5.0, nlme3.1-152, openssl1.4.4, openxlsx4.2.3, parallelly1.26.1, patchwork1.1.1, pbapply1.4-3, pillar1.6.1, pkgconfig2.0.3, plotly4.9.4.1, plyr1.8.6, png0.1-7, polyclip1.10-0, prettyunits1.1.1, progress1.2.2, promises1.2.0.1, purrr0.3.4, qvalue2.22.0, R.methodsS31.8.1, R.oo1.24.0, R.utils2.10.1, R62.5.0, RANN2.6.1, rappdirs0.3.3, RColorBrewer1.1-2, Rcpp1.0.7, RcppAnnoy0.0.18, RCurl1.98-1.3, readr1.4.0, RefManageR1.3.0, reshape21.4.4, reticulate1.18, rjson0.2.20, rlang0.4.11, rmarkdown2.7, ROCR1.0-11, rpart4.1-15, RSpectra0.16-0, RSQLite2.2.5, rstudioapi0.13, Rtsne0.15, rvest1.0.0, S4Vectors0.28.1, sass0.3.1, scales1.1.1, scattermore0.7, sceasy0.0.6, sctransform0.3.2, sessioninfo1.1.1, Seurat4.0.3, SeuratObject4.0.2, shiny1.6.0, shinycssloaders1.0.0, shinydashboard0.7.1, shinyFiles0.9.0, shinyjs2.0.0, shinyWidgets0.6.0, SingleCellExperiment1.12.0, spatstat.core2.2-0, spatstat.data2.1-0, spatstat.geom2.2-0, spatstat.sparse2.0-0, spatstat.utils2.2-0, stringi1.6.2, stringr1.4.0, SummarizedExperiment1.20.0, survival3.2-10, svglite2.0.0, systemfonts1.0.2, tensor1.5, tibble3.1.2, tidyr1.1.3, tidyselect1.1.0, utf81.2.1, uwot0.1.10, vctrs0.3.8, viridis0.6.1, viridisLite0.4.0, webshot0.5.2, withr2.4.2, xfun0.20, XML3.99-0.6, xml21.3.2, xtable1.8-4, XVector0.30.0, yaml2.2.1, zip2.1.1, zlibbioc1.36.0, zoo1.8-9

References

# Writes knitcitations references to references.bib file.
knitcitations::write.bibtex(file="references.bib")

Gandolfo, Luke C., and Terence P. Speed. 2018. “RLE Plots: Visualizing Unwanted Variation in High Dimensional Data.” Edited by Enrique Hernandez-Lemus. PLOS ONE 13 (2): e0191629. https://doi.org/10.1371/journal.pone.0191629.

Hafemeister, Christoph, and Rahul Satija. 2019. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20 (1). https://doi.org/10.1186/s13059-019-1874-1.

Tirosh, I., B. Izar, S. M. Prakadan, M. H. Wadsworth, D. Treacy, J. J. Trombetta, A. Rotem, et al. 2016. “Dissecting the Multicellular Ecosystem of Metastatic Melanoma by Single-Cell RNA-Seq.” Science 352 (6282): 189–96. https://doi.org/10.1126/science.aad0501.

Traag, V. A., L. Waltman, and N. J. van Eck. 2019. “From Louvain to Leiden: Guaranteeing Well-Connected Communities.” Scientific Reports 9 (1). https://doi.org/10.1038/s41598-019-41695-z.